Cécile Albenne

Hervé Canut

Georges Boudart

Hélène Yu Zhang

San Clemente

Rafael Pont-Lezica

Elisabeth Jamet
email: jamet@scsv.ups-tlse.fr

Plant cell wall proteomics: mass spectrometry data, a trove for research on

Keywords: cell wall protein, MALDI-TOF, mass spectrometry, post-translational modification, protein structure, proteomics

INTRODUCTION

The plant cell wall is an essential cell compartment that has structural as well as functional roles. The envelope of the plant cell is not only responsible for its shape; it is also a dynamic structure in direct contact with the environment. Mostly composed by polysaccharides (up to 95 % in mass) the cell wall also contains proteins, and in some cell types, lignins [START_REF] Carpita | Structural models of primary cell walls in flowering plants: consistency of molecular structure with the physical properties of the walls during growth[END_REF]. The composition and organization of polysaccharides have been widely described. The global architecture of this network is now well studied, and several models are available [START_REF] Willats | Pectin: cell biology and prospects for functional analysis[END_REF][START_REF] Fry | Primary cell wall metabolism: tracking the careers of wall polymers in living plant cells[END_REF][START_REF] Somerville | Toward a systems approach to understanding plant cell walls[END_REF]. Among cell wall proteins (CWPs), structural proteins such as extensins and hydroxyproline/proline-rich proteins (H/PRPs) are known to reinforce the polysaccharide networks [START_REF] Showalter | Structure and function of plant cell wall proteins[END_REF][START_REF] Kieliszewski | Extensin: repetitive motifs, functional sites, post-translational codes, and phylogeny[END_REF]. From a functional point of view, the cell wall is essential for growth and differentiation, signaling, and response to biotic and abiotic stresses [START_REF] Roberts | Structures at the plant cell surface[END_REF][START_REF] Roberts | How the cell wall acquired a cellular context[END_REF][START_REF] Ellis | The Arabidopsis mutant cev1 links cell wall signaling to jasmonate and ethylene responses[END_REF][START_REF] Vogel | Mutations in PMR5 result in powdery mildew resistance and altered cell wall composition[END_REF]. Since the true actors of cell wall dynamics are proteins, all CWPs other than structural proteins are of interest. Therefore, to better understand the cell wall complexity, the challenge is to go further into the identification of the CWPs and their structure/function relationships. In this context, the last few years saw the emergence of cell wall proteomics aimed at profiling CWPs at a given time in specific environmental conditions. Beyond some studies performed on Medicago sativa [START_REF] Watson | Proteomics of Medicago sativa cell walls[END_REF], Zea mays [START_REF] Alvarez | Characterization of the maize xylem sap proteome[END_REF][START_REF] Zhu | Cell wall proteome in the maize primary root elongation zone. I. Extraction and identification of water-soluble and lightly ionically bound proteins[END_REF], Cicer arietinum [START_REF] Bhushan | Extracellular matrix proteome of chickpea (Cicer arietinum L.) illustrates pathway abundance, novel protein functions and evolutionary perspect[END_REF], and Oryza sativa [START_REF] Chen | Proteomics of weakly bound cell wall proteins in rice calli[END_REF][START_REF] Jung | Systematic secretome analyses of rice leaf and seed callus suspension-cultured cells: Workflow development and establishment of high-density two-dimensional gel reference maps[END_REF][START_REF] Cho | The proteomic analysis of the secretome of rice calli[END_REF] most data on plant cell wall proteomics concern the model plant Arabidopsis thaliana. The knowledge on CWPs has recently been reviewed and updated [START_REF] Jamet | Cell wall proteins: a new insight through proteomics[END_REF][START_REF] Jamet | Recent advances in plant cell wall proteomics[END_REF]. As an active contributor to the description of A. thaliana CWPs [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF][START_REF] Charmont | Proteomic analysis of secreted proteins from Arabidopsis thaliana seedlings: improved recovery following removal of phenolic compounds[END_REF][START_REF] Feiz | Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls[END_REF][START_REF] Minic | A sub-proteome of Arabidopsis thaliana trapped on Concanavalin A is enriched in cell wall glycoside hydrolases[END_REF][START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF], our group has built a database (WallProtDB) containing all A. thaliana and O. sativa CWPs identified in different organs. So far, this database contains nearly 800 CWPs and is freely accessible to the public (http://www.polebio.scsv.ups-tlse.fr/WallProtDB/). CWPs in the database were classified into nine functional classes according to bioinformatic predictions, thus revealing a great diversity of protein functions inside the cell wall. We now have a good picture of CWPs present in the plant cell wall, but the exact functions of most of these proteins are still unknown. Several descriptions of CWP mutants are proof of the importance of proteins in cell walls both during plant development and in response to pathogens [START_REF] Berger | A subtilisin-like serine protease involved in the regulation of stomatal density and distribution in Arabidopsis thaliana[END_REF][START_REF] Baumberger | Whole-genome comparison of leucine-rich repeat extensins in Arabidopsis and rice. A conserved family of cell wall proteins form a vegetative and a reproductive clade[END_REF][START_REF] Vogel | Mutations in PMR5 result in powdery mildew resistance and altered cell wall composition[END_REF][START_REF] Roudier | COBRA, an Arabidopsis extracellular glycosyl-phosphatidyl inositol-anchored protein, specifically controls highly anisotropic expansion through its involvement in cellulose microfibril orientation[END_REF][START_REF] Kurdyukov | The epidermis-specific extracellular BODYGUARD controls cuticle development and morphogenesis in Arabidopsis[END_REF]. However, many topics related to CWPs still need to be addressed to achieve a better understanding of cell wall dynamics. Among these topics are: the role of different members of multigenic families, the identification of substrates or partners, the posttranslational modifications (PTMs), and the spatial and temporal regulation.

Several strategies can be used in cell wall proteomic studies [START_REF] Jamet | Recent advances in plant cell wall proteomics[END_REF]. A simplified flowchart for plant cell wall proteomics is presented in Figure 1. Defining a specific strategy to answer a biological question requires two essential decisions involving: (i) the choice of the appropriate material; and (ii) a method to prepare a protein extract containing as few intracellular proteins as possible [START_REF] Feiz | Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls[END_REF]. Since CWPs are mostly basic glycoproteins, a good protein separation was obtained simply by mono-dimensional electrophoresis (1D-E) [START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF]. However, to improve CWP fractionation, cationic exchange chromatography (CEC) performed in acidic pH was shown to be a method of choice, because it mimics the physico-chemical conditions in cell walls [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. In a second step, chromatography fractions were separated by 1D-E, thus offering a more suitable bi-dimensional (2D) separation than 2D-E. The staining of gels can be done in Three main steps are required to obtain information on proteins using MS from plant material. The first step is the collection of plant material. The second step is protein extraction and separation. Efficient fractionation of CWPs can be achieved in different ways: 1D-separation (monodimensional) only using 1D-E (monodimensional electrophoresis); or 2D-separation (bi-dimensional) using cationic exchange chromatography (CEC) followed by 1D-E of eluted fractions. In the third step, after staining, excision, protease digestion, and sample preparation, MS analysis is performed, followed by bioinformatic data processing. This leads to a flowchart that allows protein identification and gives information on maturation events and PTMs. different ways, revealing different types of proteins with different sensitivities [START_REF] Chevalier | Proteomic capacity of recent fluorescent dyes for protein staining[END_REF][START_REF] Jamet | Recent advances in plant cell wall proteomics[END_REF]. Stained bands of interest were excised from gels and subsequently submitted to protease digestion prior to analysis by MS. Two approaches are widely used for protein identification: (i) peptide mass fingerprinting (PMF) (MS spectrum) and (ii) peptide sequencing by tandem mass spectrometry (MS/MS spectrum) [START_REF] Aebersold | Mass spectrometry-based proteomics[END_REF]. The main advantage of the flowchart in Figure 1 is that it provides samples containing whole proteins ready for MS analysis. Analysis of complex samples was also performed, but is more difficult to interprete because the peptides of all the proteins are mixed [START_REF] Bayer | Arabidopsis cell wall proteome defined using multidimensional protein identification technology[END_REF]. Reconstruction of whole proteins requires more powerful software, and the structural information for a given protein is more difficult to obtain. Each step described in Figure 1 can be optimized to yield better results. In addition, MS technologies have evolved very quickly these last years, and they offer a large choice of sources of ionization and of analyzers [START_REF] Aebersold | Mass spectrometry-based proteomics[END_REF][START_REF] Han | Mass spectrometry for proteomics[END_REF]. However, to identify and characterize proteins, MALDI-TOF MS and MALDI-TOF-TOF MS/MS remain powerful, easy to use methods. They are the only two techniques to be considered in this paper. Finally, the bioinformatic analysis is critical because it allows not only protein identification, but also prediction of protein sub-cellular location and functional domains [START_REF] San Clemente | Bioinformatics as a tool for assessing the quality of sub-cellular proteomic strategies and inferring functions of proteins: plant cell wall proteomics as a test case[END_REF].

Although proteomics has been shown to be a very valuable way to identify CWPs, mass spectrometry (MS) data can yield more information on protein structure, location of PTMs and processing, thus tackling biochemical protein complexity and revealing the hidden part of the iceberg. This article seeks to reveal the potential capacity of MS to improve our knowledge of CWPs. Relevant examples were chosen from a previous cell wall proteomic study in etiolated A. thaliana hypocotyls, where 137 CWPs were identified [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. A detailed analysis of the data allowed the discrimination of close members of multigenic families, highlight maturation events, locate PTMs, or access proteins which are recalcitrant under standard conditions.

RESULTS AND DISCUSSION

Discrimination among close members of multigenic families

Most plant genes including those encoding CWPs, belong to multigene families. These can be very large, and they can include closely related members. For example, in A. thaliana there are 73 predicted peroxidase genes [START_REF] Oliva | PeroxiBase: a powerful tool to collect and analyse peroxidase sequences from Viridiplantae[END_REF], 66 pectin methylesterase (PME) genes (http://cellwall.genomics.purdue.edu/families/4-5-1.html), 33 xyloglucan endotransglucosylase/hydrolase genes [START_REF] Rose | The XTH family of enzymes involved in xyloglucan endotransglucosylation and endohydrolysis: current perspectives and a new unifying nomenclature[END_REF] and 66 polygalacturonase genes (http://cellwall.genomics.purdue.edu/families/4-3-3.html). It is usually difficult to study the regulation of close members of these gene families at the protein level. Classical approaches using immunology are not efficient because of high amino acid similarity, but PMF using MALDI-TOF MS provides a powerful alternative. Indeed, subtle mass differences between peptides can be measured, the accuracy of mass determination being as small as 20 ppm (parts per million). As an example, the identification in the same sample of two closely related lectin homologues is reported in Figure 2. At1g78850 and At1g78860 share 93% amino acid identity. On MS spectra, seven peptides common to both proteins were found (boxed peptides) whereas four and three peptides were respectively specific to At1g78850

(underlined in green) and At1g78860 (underlined in blue) (Supplementary Figure 1). Note that in one case, the two versions of a peptide could be easily distinguished (ILENGNMVIYDSS/NGK), that is, a difference of 11.02 Da in mass (1655.79 Da and 1666.81 Da respectively). In our study, all CWPs could be identified unambiguously, but some intracellular contaminants of our cell wall preparation could not be discriminated because they belong to multigene families comprising closely related members. Either there was not enough protein coverage to find specific peptides, or the amino acid sequences of the proteins were too close, e.g. malate dehydrogenase (88% shared identity between At1g53240 and At3g15020), RUBISCO small subunit (96% or 97% shared identity among At5g38410, At5g38420, and At5g38430), actin (100% shared identity between At2g37620 and At3g53750). The last case was that of proteins with repetitive domains such as polyubiquitins and ubiquitin extension proteins (e.g. At3g52590, At2g36170, At2g35635, and At1g31340). The amino acid sequences of two A. thaliana lectin homologues At1g78850 (NP_178006) and At1g78860 (NP_178007) were superimposed using the WebLogo 3 software (http://weblogo.threeplusone.com/). Common amino acids are identified with a single capital letter and differences in amino acid sequences appear in small superimposed letters (upper line for NP_178006, lower line for NP_178007). Black frames express common peptides found by protein PMF. Peptides discriminating between NP_178006 and NP_178007 are respectively underlined in green and blue. The positions and the monoisotopic masses of the peptides are written on the right in black, green, and blue for peptides common to both proteins, specific to NP_178006, and specific to NP_178007, respectively.

A site mapping approach to post-translational maturation events

To become biologically active, some enzymes need to lose a pro-sequence, and are submitted to post-translational maturation events. PMF can be used as a tool to show such events by mapping the identified peptides on the sequence of the proteins. To be reliable, it must be carried out on several independent samples. Two examples of possible CWP maturation events are shown in Figure 3. In our proteomic study on etiolated A. thaliana hypocotyls, six different putative pectin methylesterases (PMEs) were identified [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. Five of them are type-I PMEs with both a predicted N-terminal pectinesterase inhibitor domain (PMEI, PF04043), and a Cterminal pectinesterase domain (PME, PF01095) [START_REF] Micheli | Pectin methylesterases: cell wall enzymes with important roles in plant physiology[END_REF]. The cleavage of the prosequence was shown to depend on the presence of one or two conserved basic tetrad motifs [START_REF] Wolf | The N-terminal pro region mediates retention of unprocessed type-I PME in the Golgi apparatus[END_REF]. In none of the cases did we find peptides in the PMEI domains. In seven independent identifications of At1g53830 (Figure 3A), all the peptides were mapped to the predicted PME domain, i.e. between amino acids 275 and 548 thus allowing a coverage of 47% of this region (Supplementary Figure 2). In addition, the apparent molecular masses of these PMEs after 1D-E in denaturing conditions was around 35 kDa, consistent with a maturation event of the protein. For a long time, it was thought that there was no PMEI domain in cell wall PMEs. It was assumed that PMEI domains of PMEs could inhibit their enzyme activity thus preventing the premature de-esterification of pectins before their secretion [START_REF] Micheli | Pectin methylesterases: cell wall enzymes with important roles in plant physiology[END_REF]. However, it was recently shown that maturation of PMEs inside the secretory pathway is required for their secretion [START_REF] Bosch | Pectin methylesterase, a regulator of pollen tube growth[END_REF][START_REF] Wolf | The N-terminal pro region mediates retention of unprocessed type-I PME in the Golgi apparatus[END_REF]. Our results are consistent with the single presence of mature PMEs in cell walls. They strongly support the assumption that maturation occurs before protein secretion. However, this must be confirmed by Edman degradation sequencing to provide the actual N-terminus of the processed protein.

The second example of protein maturation is provided by α-xylosidase At1g68560. In our samples, α-xylosidase was repeatedly identified as the only component present in three wide

Coomassie blue stained bands obtained after 1D-E in denaturing conditions (H1, H2, H3 in Figure 3B) [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. Apparent molecular masses were between 75 and 100 kDa.

Cartography of identified peptides on 18 independent fractions indicates that proteins in H3 lack N-terminal peptides (Supplementary Figure 3). Differences in molecular masses between H1, H2, and H3 are thus probably due either to the N-terminal processing of the protein, or to N-glycosylations. In fact, there are eight predicted N-glycosylation sites in the protein. We found that at least one of these sites is occupied. Stars in Figure 3B point to N-glycosylated forms of the protein on the SNHETLFNTTSSLVFK peptide located between amino acids 156 and 173. In addition, At1g68560 was identified in a previous proteomic study performed on a stem protein fraction retained on Concanavalin A [START_REF] Minic | A sub-proteome of Arabidopsis thaliana trapped on Concanavalin A is enriched in cell wall glycoside hydrolases[END_REF]. We suspect that different states of N-glycosylation may occur on the other predicted sites. Furthermore, our MS data were compared to results of the N-terminal sequencing of a cabbage α-xylosidase, which is also a member of Brassicaceae. This enzyme shows 75% identity with At1g68560 and an apparent molecular mass of 85 kDa after 1D-E in denaturing conditions [START_REF] Sampedro | Cloning and expression pattern of a gene encoding an α-xylosidase active against xyloglucan oligosaccharides from Arabidopsis[END_REF]. Edman sequencing showed this N-terminal sequence was ISGSELTFSYTTDPFSFAVKRRL (J Sampedro, personal communication). It shares 87% identity with an At1g68560 peptide located between amino acids 123 and 146. The removal of the peptide upstream of this sequence would cause a mass shift of 10.9 kDa, which fits in with the difference observed between molecular masses of H2 and H3. Altogether, it suggests that At1g68560 is processed at its N-terminus in cell walls. The processing of cell wall enzymes that act on polysaccharides was already shown. An α-L-arabinofuranosidase (ARAI) and a β-D-xylosidase (XYL) from barley are post-translationally processed at their Cterminus [START_REF] Lee | Bifunctional family 3 glycoside hydrolases from barley with α-L-arabinofuranosidase and β-D-xylosidase activity. Characterization, primary structures, and COOH-terminal processing[END_REF]. Such C-terminal processing was also suggested for the β-Dxylosidases AtXYL1 and AtXYL4 [START_REF] Minic | Purification and characterization of enzymes exhibiting β-D-xylosidase activities in stem tissues of Arabidopsis[END_REF]. In this case, not all the C-termini of the proteins were covered by MALDI-TOF MS PMF. An endo-β-1,4-xylanase (XYN-1) of barley aleurone was shown to be processed both at its N-and C-termini [START_REF] Caspers | Synthesis, processing and export of cytoplasmic endo-β-1,4-xylanase from barley aleurone during germination[END_REF].

In addition, cell wall proteomic studies have shown that proteases represent about 11.6% of identified CWPs, none of which have known substrates [START_REF] Jamet | Recent advances in plant cell wall proteomics[END_REF]. In conclusion, our data support the hypotheses of an N-terminal processing of the At1g68560 α-xylosidase in muro and of the presence of N-glycans.

In order to facilitate MS data analysis and easily identify post-translational maturation or modification events, we developed a bioinformatic tool called ProTerNyc, which is freely accessible (http://www.polebio.scsv.ups-tlse.fr/ProTerNyc/). This software predicts the location of the N-terminus in mature secreted protein and the N-glycosylated peptides using the data obtained by MS. The N-terminus is predicted by TargetP [START_REF] Emanuelsson | Locating proteins in the cell using TargetP, SignalP and related tools[END_REF].

With this tool it is possible to overcome the limitations of searches in databases that do not take into account the signal peptide cut or peptide glycosylation. After running ProTerNyc on a given protein sequence and on a mass list generated by MS experiments, it can be seen that some observed peaks can be attributed to putative N-terminal peptide and N-glycopeptides.

Location of post translational modifications

Aside from all the PMF data used for the cartography of a protein sequence, it is sometimes very instructive to go further into the MS spectra. When exploring the MS data for etiolated hypocotyl CWP fractions [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF], we observed some significant peaks not always used for protein identification. A detailed analysis of these data revealed that they are consistent with PTMs such as N-glycosylation or amino acid hydroxylation or oxidation.

N-glycosylations

Several peroxidases were found [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. Figure 4A shows the MS spectrum allowing the identification of one of these peroxidases, encoded by At3g32980. Among the major peaks displayed in this spectrum, five were found to fit with non-modified peptides of At3g32980, leading to its identification. Other peaks in this spectrum were shown to belong to other proteins also identified in this sample, except for four peaks of high m/z (2661. 16, 2864.25, 3685.54, and 3888.65). Since At3g32980 carries four putative N-glycosylation sites, we looked for possible glycopeptides. Using the m/z of the four peaks listed above (red arrows in Figure 4A) and those of canonical N-glycans observed in plants [START_REF] Faye | Protein modifications in the plant secretory pathway: current status and practical implications in molecular pharming[END_REF], we calculated that each of these four peaks may correspond to two modified peptides, P1 and P2. Both peptides possess one N-glycosylation consensus sequence and are supposed to carry two different complex motifs of N-glycosylation. Interestingly, in their unmodified form P1 and P2 were absent from the spectrum suggesting that in our experimental conditions these glycosylation sites are always occupied. To confirm that these four peaks correspond to glycopeptides, we analyzed a total protein extract after hydrogen fluoride (HF)

deglycosylation. MS analysis of the corresponding sample showed that all four peaks were absent (Figure 4B). This suggests that glycosylations were removed during the HF treatment, confirming the nature of these modifications.

It has been reported that N-glycans are asymmetrically distributed on soybean seed coat peroxidase, whereas they are more uniformly distributed over horseradish peroxidase [START_REF] Gray | Heterogeneity of glycans at each N-glycosylation site of horseradish peroxidase[END_REF][START_REF] Gray | Asymmetric glycosylation of soybean seed coat peroxidase[END_REF]. In addition, a study performed on cationic peanut peroxidase showed that N-glycans are essential to attain full catalytic ability and thermostability, and to influence protein folding [START_REF] Lige | The effects of the site-directed removal of N-glycosylation from cationic peanut peroxidase on its function[END_REF]. Other examples demonstrate the importance of protein N-glycosylation in plants. The presence of N-glycans on the jack bean α-mannosidase was shown to influence the enzyme activity as well as its oligomerization [START_REF] Kimura | The N-glycans of jack bean α-mannosidase[END_REF]. It was also proposed that N-glycosylation motifs on a plant cell wall polygalacturonase inhibitor protein (PGIP) may favor interaction with its fungal polygalacturonase target during host-pathogen interaction [START_REF] Lim | Mapping glycans onto specific N-linked glycosylation sites of Pyrus communis PGIP redefines the interface for EPG-PGIP interactions[END_REF]. Finally, several studies on defective glycosylation mutants have shown that the N-glycosylation pathway regulates protein quality control, salt tolerance, cell wall deposition, development, and plant-pathogen interactions [START_REF] Pattison | N-glycan production in the endoplasmic reticulum of plants[END_REF]. corresponds to the addition of the mass of the peptide and that of the consensus N-glycans represented schematically as described [START_REF] Faye | Protein modifications in the plant secretory pathway: current status and practical implications in molecular pharming[END_REF]. A zoomed spectrum (m/z from 2500 to 4000) of the deglycosylated sample shows that glycopeptides are absent.

Our results show that MS can be used to predict glycosylation and to gain new insight into the structural and functional characterization of proteins. However, until now, it was difficult to recognize N-glycopeptides using a systematic proteomic approach. putative N-glycopeptides and to identify them in a mass list obtained from MS experiments. It should be noted that mass tolerance for peptides with high m/z may need to be increased since accuracy of mass determination decreases as mass increases. This software will undoubtedly be a precious tool to search for glycopeptides.

Amino acid hydroxylation or oxidation

Many CWPs contain hydroxylated Pro (Hyp), which can be O-glycosylated [START_REF] Kieliszewski | Extensin: repetitive motifs, functional sites, post-translational codes, and phylogeny[END_REF]. The At3g16850 polygalacturonase was identified by PMF using twelve peptides among CWPs from etiolated hypocotyls [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. Among these peptides, one (P peptide) was predicted to exist under several forms. Indeed, the mass spectrum displays an isotopic massif with a monoisotopic peak at m/z 2522.23, associated with two isotopic massifs at a distance of 16 Da and 32 Da respectively (Figure 5). Such modifications may respectively correspond to the oxidation or hydroxylation of one or two amino acids. The P peptide has a theoretical monoisotopic mass of 2522.27 Da (Figure 5). We performed MALDI-TOF-TOF MS/MS experiments on each of these peptides in order to confirm that these three isotopic massifs correspond to the same peptide, and locate oxidations or hydroxylations of amino acids. A clear fragmentation pattern was obtained from the P peptide, confirming the proposed sequence (Supplementary Figure 4). Both MS/MS spectra of the modified forms (m/z 2583.23 and 2554.21) present the two most abundant fragments previously observed for the P peptide (y10 and y15, Supplementary Figure 5). However, one of these fragments (y15) differed by 16 Da or 32 Da from the corresponding peaks found on the P peptide fragmentation pattern. These results confirmed the presence of oxidations or hydroxylations and allowed their localization in a region of 5 possible amino acid residues (PSHWD, in bold in Figure 5). The oxidized or hydroxylated amino acids in this sequence probably are the Pro, His, Trp and Asp, which respectively give rise to Hyp, oxohistidine, hydroxytryptophan and hydroxyaspartate. Hyp is widespread in the plant kingdom. Pro hydroxylation is a PTM performed by prolyl 4-hydroxylases in the endoplasmic reticulum [START_REF] Lamport | The protein component of primary cell walls[END_REF]. A code based on primary sequences was proposed for Pro hydroxylation and consecutive O-glycosylation in Hyp-rich glycoproteins (HRGPs) and in Hyp/Pro-rich proteins (H/PRPs) [START_REF] Shpak | Synthetic genes for glycoprotein design and the elucidation of hydroxyproline-O-glycosylation codes[END_REF][START_REF] Kieliszewski | The latest hype on Hyp-O-glycosylation codes[END_REF]. In the case of At3g16850, only one Pro may be hydroxylated in the P peptide within the Asp-Pro-Ser context which has never been described in plant proteins. In addition, the presence of Hyp in a CWP different from the wellknown HRGPs and H/PRPs is noteworthy. A few cases have been reported in the literature, such as that of the dodeca-CLE peptides involved in cell differentiation [START_REF] Ito | Dodeca-CLE peptides as suppressors of plant stem differentiation[END_REF][START_REF] Kondo | A plant peptide encoded by CLV3 identified by in situ MALDI-TOF MS analysis[END_REF]. Hyp was found in the Val-Pro-Ser and Gly-Pro-Asn sequences. So far, Pro hydroxylation in the CLE peptides seems to have no effect on their biological activity.

Unlike Hyp, the presence of oxohistidine, hydroxytryptophan, and hydroxyaspartate in a plant protein has never been reported. Several examples of metal-catalyzed oxidation of His to oxohistidines were described for human proteins [START_REF] Schöneich | Mechanisms of metal-catalyzed oxidation of histidine to 2-oxo-histidine in peptides and proteins[END_REF][START_REF] Hovorka | High sensitivity of Zn 2+ insulin to metal-catalyzed oxidation: detection of 2-oxo-histidine by tandem mass spectrometry[END_REF].

Hydroxytryptophan is described as a precursor of the neurotransmitter serotonin and as an intermediary in tryptophan metabolism [START_REF] Birdsall | 5-Hydroxytryptophane: a clinically-effective serotonin precursor[END_REF]. Hydroxyaspartate residues were found in epidermal growth factor domains of several mammalian proteins. It was proposed that this modification regulates negatively the fucosylation of these regions [START_REF] Castellino | γ-glutamate and β-hydroxyaspartate in proteins[END_REF]. In plants, oxidative modifications are known to occur in presence of reactive oxygen species and metals, in particular in cell walls [START_REF] Møller | Oxidative modifications to cellular components in plants[END_REF]. In the case of At3g16850, suspected oxidized or hydroxylated amino acids do not belong to the active site according to bioinformatic prediction. We can note that in the closest plant homologs of At3g16850 the pentapeptide DPSHW is conserved, suggesting its importance for protein function. However, since no structural data are available and no catalytic residues have been identified for any plant polygalacturonase, it is still difficult to predict a role for these amino acids. The protein was identified in the Q CWP fraction obtained by CEC [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. PMF identification was achieved with 12 peptides among which the P peptide (sequence and mass indicated in frame). The range of the spectrum zoom shown (m/z between 2515 and 2563) corresponds to the range where P is observed, with no modification or with one or two modifications (mass shifts of 16 Da and 32 Da respectively). The two amino acid modifications are assumed to occur within the sequence indicated in bold.

Identification and characterization of recalcitrant structural CWPs

In all the plant cell wall proteomes described so far, structural cell wall glycoproteins such as extensins and H/PRPs represent only 1.7 % of the presently identified CWPs and remain under-represented [START_REF] Jamet | Recent advances in plant cell wall proteomics[END_REF]. Many reasons may explain this situation: (i) the extraction of structural proteins is difficult since they can be strongly bound to the cell wall;

(ii) the separation by electrophoresis in denaturing conditions can be disturbed by the presence of glycosylations; (iii) digestion into peptides suitable for standard MS experiments can be limited because of the low frequency of protease cut sites into repetitive sequences, rendering protein identification by PMF difficult. Alternative strategies must be used to identify and characterize these proteins, such as peptide sequencing by tandem MS and MS in linear mode.

The At5g14920 H/PRP, also named AtGASA14 [START_REF] Roxrud | GASA4, one of the 14member Arabidopsis GASA family of small polypeptides, regulates flowering and seed development[END_REF], was identified in etiolated hypocotyls and a smear was observed after 1D-E, with an apparent molecular mass between 35 and 40 kDa (between K14 and K11 bands in Figure 6A) [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF].

Aside from the Pro-rich region covering most of the protein, At5g14920 also contains a cleavable N-terminal signal peptide and a C-terminal sequence which shows homology with the GASA peptides previously described in plants (Figure 6B). Analysis of the primary sequence of the Pro-rich region indicates that it contains only two tryptic cut sites since this protease cannot cleave Lys-Pro linkages. Altogether, in the entire sequence, only 12 Lys/Arg residues can be used for tryptic digestion (in bold in Figure 6B). In addition, some cleavage sites are very close together, generating peptides too small to be observed in the standard reflectron mode MALDI-TOF m/z window between 750 and 4000 Da. Instead, the two tryptic cut sites in the Pro-rich region release two large tryptic peptides, P1 and P2, which are also out of that m/z range. Despite these sequence constraints, three peptides from the C-terminal sequence, suitable for the standard reflectron mode MALDI-TOF MS, were found in the previously obtained spectra (Figure 6B). Their sequences were confirmed by MALDI-TOF-TOF MS/MS fragmentation (Supplementary Figure 6). In addition, N-terminal sequencing by

Edman degradation [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF] enabled to localize the N-terminus of the protein downstream the N-terminus predicted by bioinformatics, revealing a possible N-terminal processing or degradation. Thus both tandem MS and Edman degradation results allowed to correctly identify At5g14920.

In order to find the P1 and P2 peptides in the Pro-rich region of At5g14920, we performed additional MALDI-TOF MS experiments, this time in linear mode instead of the reflectron mode used in PMF standard conditions. The presence of consecutive Pro in the Pro-rich region suggests that, similarly to other cell wall H/PRPs [START_REF] Kieliszewski | Extensin: repetitive motifs, functional sites, post-translational codes, and phylogeny[END_REF], some of them may be hydroxylated. Up to 17 and 39 Hyp residues can be expected on P1 and P2 respectively, respectively increasing their mass up to 272 and 624 Da. This calculation excludes Hyp in the Leu-Pro and Lys-Pro contexts according to previous work [START_REF] Kieliszewski | Extensin: repetitive motifs, functional sites, post-translational codes, and phylogeny[END_REF][START_REF] Shimizu | Experimental determination of proline hydroxylation and hydroxyproline arabinogalactosylation motifs in secretory proteins[END_REF]. The MS spectrum for K11 clearly shows two major peaks in the m/z range 5700-18000 (Figure 6C). The peak centered on the mean m/z 15400 possibly corresponds to P2. This m/z, which is higher than the peptide theoretical mass (between 11859 and 12483 Da depending on the number of Hyp), suggests the presence of Oglycosylations in this region. In addition, the width of the observed peak (m/z 14200-16600) reflects mass heterogeneity, probably due to different states of O-glycosylation. The diprotonated form of P2 (m/z 7700) is also observed. However, the corresponding peak in the m/z range 7000-8800 may also contain the signal of the mono-protonated form of P1 which can overlap with that of the di-protonated P2. The spectrum obtained for K14 has a peak at m/z 13070, i.e. within the m/z 12500-14000 range corresponding to P2. Interestingly, for this peptide a shift of mass of about 2300 Da is observed between K11 and K14, indicating a lower level of O-glycosylation in K14. In K14, the di-protonated form of P2 is resolved from the mono-protonated form of P1, at m/z 6500. The peak centered on m/z 7200, i.e. within the m/z 7000-7500 range, may be attributed to the mono-protonated form of P1. This mass range coincides with the expected mass of P1 calculated in the N-terminus determined by Edman degradation (up to 6866 Da, Figure 6B).

Considering the primary sequence of At5g14920 and the known codes for hydroxylation of Pro and subsequent O-glycosylations, At5g14920 may be O-gycosylated in different ways.

The presence of the Thr-Pro modules suggests the presence of arabinogalactans like in AGPs [START_REF] Tan | Glycosylation motifs that direct arabinogalactan addition to arabinogalactan-proteins[END_REF]. However, the staining of the protein with the Yariv reagent was negative (Supplementary Figure 7). The presence of Lys-Pro-Pro-Thr motifs like in the maize THRGP (threonine-hydroxyproline-rich glycoprotein) suggests the presence of arabinosylated Hyp [START_REF] Kieliszewski | Purification and partial characterization of a hydroxyproline-rich glycoprotein in a graminaceous monocot, Zea mays[END_REF][START_REF] Kieliszewski | Structure of the threonine-rich extensin from Zea mays[END_REF]. However, similar motifs are present in AtAGP31 and in NaPRP4 which were found to contain 80 and 83% of galactose respectively [START_REF] Sommer-Knudsen | A galactose-rich, cell-wall glycoprotein from styles of Nicotiana alata[END_REF][START_REF] Liu | A nonclassical arabinogalactan protein gene highly expressed in vascular tissues, AGP31, is transcriptionally repressed by methyl jasmonic acid in Arabidopsis[END_REF]. Staining of a fraction enriched in At5g14920 with a lectin specific for galactose was positive indicating the possible presence of galactose (Supplementary Figure 7). Finally, the presence of both arabinose and galactose cannot be excluded as for the Chlamydomonas reinhardtii GP1 HRGP [START_REF] Ferris | Glycosylated polyproline II rods with kinks as a structural motif in plant hydroxyproline-rich glycoproteins[END_REF]. The MS results reported here suggest the presence of O-glycosylation on an H/PRP. As for NaPRP4 and AtAGP31 [START_REF] Sommer-Knudsen | A galactose-rich, cell-wall glycoprotein from styles of Nicotiana alata[END_REF][START_REF] Liu | A nonclassical arabinogalactan protein gene highly expressed in vascular tissues, AGP31, is transcriptionally repressed by methyl jasmonic acid in Arabidopsis[END_REF], varying degrees of O-glycosylation were observed for At5g14920. The carbohydrate content of At5g14920 is estimated to be at much as 25 %, under that observed for NaPRP4 and AtAGP31 (75% and 80 % respectively), but much higher than that observed for soybean PRP2 (less than 1%) [START_REF] Datta | Characterization of two soybean repetitive proline-rich proteins and a cognate cDNA from germinated axes[END_REF]. A functional study of glycosylations should prove interesting since the role of O-glycosylation of H/PRPs has so far never been investigated. In the case of extensins, O-glycosylation was shown to keep proteins in an extended conformation [START_REF] Stafstrom | The role of carbohydrate in maintaining extensin in an extended conformation[END_REF]. Further work is necessary to elucidate the structure of the Pro-rich region and of its glycosylations [START_REF] Kieliszewski | The latest hype on Hyp-O-glycosylation codes[END_REF][START_REF] Schultz | Post-translational modifications of arabinogalactan-peptides of Arabidopsis thaliana[END_REF][START_REF] Tan | Structure of a hydroxyproline (Hyp)arabinogalactan polysaccharide from repetitive Ala-Hyp expressed in transgenic Nicotiana tabacum[END_REF][START_REF] Shimizu | Experimental determination of proline hydroxylation and hydroxyproline arabinogalactosylation motifs in secretory proteins[END_REF]. However, other biochemical approaches should be considered since MS or MS/MS alone will not fully reveal the structural features of this recalcitrant protein, such as the location of Hyp residues.

Mass spectrometry as a diagnosis tool for protein structure

The various examples presented in this article illustrate that MS is a powerful tool to gain new insight into the structure of CWPs in relation to their function. However, MS does not always allow the full coverage of a protein sequence, the complete description of PTMs, and the identification of all the proteins in a complex protein mixture. It appears that some proteins or peptides present in a sample fail to be analyzed. In certain cases, the MS results should be confirmed with additional experiments. N-or C-terminal sequencing can be used to determine the location of the ends of a protein, and consequently uphold biological processes such as maturation events. To go further into the three-dimensional structure of proteins, other methods can be used such as nuclear magnetic resonance (NMR) or X-ray crystal diffraction [START_REF] Liu | Recent development in structural proteomics for protein structure determination[END_REF]. However, the amount of purified proteins required for these analyses can be limiting, as compared to MS. Chemical proteomics that consists in the analysis by MS of proteins specifically tagged at their site of modification, is an emerging field that offers striking insights into the functional biology of PTMs [START_REF] Tate | Recent advances in chemical proteomics: exploring the post-translational proteome[END_REF]. In conclusion, MS will increasingly be used as a diagnosis tool to address biological questions given its simple use, high throughput capacity, unequaled sensitivity, and the continuous technical developments in the field.

METHODS

Plant material and cell wall isolation

Arabidopsis thaliana seeds (ecotype Columbia 0) were grown in the dark as described [START_REF] Feiz | Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls[END_REF][START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF], and etiolated hypocotyls were collected at 5 and 11 days. One to 1.8 cm hypocotyls were used to isolate cell walls as previously reported [START_REF] Feiz | Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls[END_REF].

The obtained cell wall fraction was ground in liquid nitrogen in a mortar with a pestle prior to lyophilization.

Cell wall protein extraction and separation

Proteins were extracted from the cell wall preparation by successive steps of 0.2 M CaCl 2 followed by 2 M LiCl. The proteins were separated by cation exchange chromatography (CEC). The column fractions were separated by 1D-electrophoresis (1D-E), stained and named as previously described [START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. The bands stained with Coomassie Brilliant blue were excised from the gels and digested with trypsin as previously described [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF]. Staining of proteins with the β-glycosyl Yariv reagent was performed as previously described [START_REF] Willats | A role for arabinogalactan-proteins in plant cell expansion: evidence from studies on the interaction of β-glucosyl Yariv reagent with seedlings of Arabidopsis thaliana[END_REF]. Staining of proteins with the galactose-specific peanut agglutinin (PNA) lectin was carried out using the DIG Glycan Differentiation Kit procedure (Roche, Mannheim, Germany).

Peptide mass fingerprints (PMFs) were compared to the non-redundant database of A. The ProTerNyc software was developed in PHP5. It includes an N-glycan database associated with their molecular masses according to [START_REF] Faye | Protein modifications in the plant secretory pathway: current status and practical implications in molecular pharming[END_REF]. From a protein sequence and a mass list, ProTerNyc allows (i) prediction of a signal peptide using TargetP

Figure 1 .

 1 Figure 1. A simplified plant cell wall proteomic flowchart.

Figure 2 .

 2 Figure 2. Identification of members of multigene families by peptide mass mapping.

Figure 3 .

 3 Figure 3. Looking for post-translational maturation events: At1g53830 (NP_175786) encoding a pectin methylesterase (A) and At1g68560 (NP_177023) encoding an αxylosidase (B) as test cases.In A, At1g53830 is represented as a scheme showing four predicted domains: a signal peptide (between amino acids 1 and 34, dotted box); a pectin methylesterase inhibitor domain (PMEI, between amino acids 56 and 215, striped box); a pectin methylesterase domain (PME, between amino acids 273 and 573, black box). RRLK and RRLL located between amino acids 242 and 265 (grey box) are the two conserved basic tetrad motifs predicted to be essential for the cleavage of the PMEI domain. The occurrence of peptides within the PME domain in seven independent PMF experiments is indicated. In B, At1g68560 is represented as a scheme showing two predicted domains: a signal peptide (between amino acids 1 and 28, dotted box); and a glycosyl hydrolase family 31 (GH31) domain (between amino acids 264 and 765). Six samples were taken from the H fraction separated by 1D-E after CEC described in[START_REF] Irshad | A new picture of cell wall protein dynamics in elongating cells of Arabidopsis: confirmed actors and newcomers[END_REF]. The limits of PMF sequence coverage of the protein in samples H1, H2 and H3 are indicated. Stars indicate the position of an N-glycosylated peptide only found in the upper parts of bands H1, H2 and H3. In A and B, white boxes on the schemes of the proteins correspond to regions where no specific domain was predicted.

Figure 4 .

 4 Figure 4. Identification of N-glycopeptides on At3g32980 (NP_850652) peroxidase by MALDI-TOF MS analysis. The protein was identified in the A CWP fraction obtained by CEC (Irshad et al., 2008) (A) and in a total CWP extract deglycosylated by hydrogen fluoride (B). Non modified peptides used for identification are shown with blue arrows and N-glycopeptides with red arrows. Two peptides (P1 and P2) carrying N-glycosylation consensus sequences (in red in the frame) were shown to carry N-glycosylations. The mass of these N-glycopeptides

Figure 5 .

 5 Figure 5. Location of amino acid oxidation or hydroxylation on the At3g16850 (NP_188308) polygalacturonase by MALDI-TOF MS analysis.

Figure 6 .

 6 Figure 6. Identification and characterization of the At5g14920 H/PRP (NP_196996)

(

  http://www.cbs.dtu.dk/services/TargetP/), and (ii) prediction of peptides possibly Nglycosylated using PROSITE (http://www.expasy.org/prosite/). In a first step, ProTerNyc predicts the length of the signal peptide. When the prediction is positive, a search for the presence of N-glycosylation motifs (PS00001) is carried out. The protein is cleaved //prospector.ucsf.edu/cgibin/msform.cgi?form=msdigest). The list of masses obtained after theoretical digestion and eventually N-glycosylations on predicted consensus sites is compared to the list of experimental masses obtained after MALDI-TOF MS analysis with a mass tolerance up to 60 ppm. The software accepts a tolerance for the length of the signal peptide around the position predicted by TargetP. The results are given in a file that can be exported in the MicrosoftOfficeExcel format (http://www.microsoft.com/france/office/2007/programs/excel/overview.mspx).

  thaliana at NCBI (http://www.ncbi.nlm.nih.gov/) using ProteinProspector MS-FIT (http://prospector.ucsf.edu/cgi-bin/msform.cgi?form=msfitstandard). The criteria used for the database search were: a monoisotopic mass accuracy of 20 ppm; one missed cleavage; and hydroxylation of Pro residues. MS/MS results were analyzed using Protein Prospector MS-Product (http://prospector.ucsf.edu/cgi-bin/msform.cgi?form=msproduct).

	Bioinformatics
	Sub-cellular location, length of signal peptides, prediction of transmembrane domains,
	homologies to other proteins and protein functional domains were predicted as previously
	described	(Minic	et	al.,	2007)	or	using	the	ProtAnnDB	database

(http://www.polebio.scsv.ups-tlse.fr/ProtAnnDB/). ProtAnnDB collects predictions of subcellular location and of functional domains obtained with different software

[START_REF] San Clemente | Bioinformatics as a tool for assessing the quality of sub-cellular proteomic strategies and inferring functions of proteins: plant cell wall proteomics as a test case[END_REF]

. N-glycosylation consensus sequences were determined using Prosite (http://www.expasy.org/prosite/). Functional domains were predicted by InterProScan (http://www.ebi.ac.uk/Tools/InterProScan/). All A. thaliana and O. sativa cell wall proteomic data are collected in the dedicated WallProtDB database (http://www.polebio.scsv.upstlse.fr/WallProtDB/).

ACKNOWLEDGEMENTS

This study was funded by the Université Paul Sabatier in Toulouse and the Centre National de la Recherche Scientifique (France). The authors are grateful to Prof M Kielieszewski (University of Ohio, USA) for welcoming GB in her laboratory for HF deglycosylation of protein samples, and to Drs J Sampedro (University of Santiago de Compostela, Spain), Z Minic (University of Saskatchewan, Canada), and J Pelloux (University of Amiens, France) for stimulating discussions. Their gratitude also goes to G Borderies and C Pichereaux for technical assistance. All MS analyses were performed on the Plate-forme Protéomique Toulouse Midi-Pyrénées, France. The authors thank D Mosovich for editing the English of the text.

Anhydrous hydrogen fluoride (HF) deglycosylation

A sample of salt extractable CWP was HF-deglycosylated for 1 h at 4 °C as described [START_REF] Mort | Anhydrous hydrogen fluoride deglycosylates glycoproteins[END_REF][START_REF] Shpak | Synthetic genes for glycoprotein design and the elucidation of hydroxyproline-O-glycosylation codes[END_REF]. The HF was blown off under nitrogen gas, and the deglycosylated proteins were then separated by 1D-E.

Mass spectrometry (MS) analyses

Sample preparation for all MALDI MS analyses was performed as previously described [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF]. MALDI-TOF MS analyses were performed using a Voyager-DE STR mass spectrometer (Applied Biosystems/MDS, Sciex, USA). Spectra were either acquired in reflectron mode as previously reported [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF]


Oussama Sbai
email: oussama.sbai@imt-nord-europe.fr

Benjamin Allaert
email: benjamin.allaert@imt-nord-europe.fr

Patrick Sondi
email: patrick.sondi@imt-nord-europe.fr

Ahmed Meddahi
email: ahmed.meddahi@imt-nord-europe.fr

SIP-DDoS: SIP Framework for DDoS Intrusion Detection based on Recurrent Neural Networks

Keywords: SIP protocol, DoS/DDoS, SIP-INVITE flooding attack, Recurrent Neural Networks, IoT

The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks has significantly driven the growth of Internet of Things (IoT) applications. These applications are characterised by: a massive connectivity, high security level, trust, wireless coverage, also ultra-low latency, high throughput, and ultra-reliability, especially for real-time oriented sessions or sensor like cameras. While traditional protocols like MQTT and CoAP are inadequate for such types of applications, under certain conditions, the 3GPP standard Session Initiation Protocol (SIP) emerges as a promising solution. However, SIP faces various Distributed Denial of Service (DDoS) threats, as INVITE flooding attacks presenting a significant challenge. This work presents a GRUbased Intrusion Detection System (IDS) to detect SIP-INVITE flooding attacks. Leveraging recurrent neural networks, the IDS efficiently process sequential SIP traffic data in real time, identifying attack patterns effectively. The GRU's ability to capture temporal dependencies enhances accuracy in classifying and detecting attack behaviors. The results demonstrate that the framework can effectively detect and mitigate INVITE flooding attacks of different intensities, under practical settings. The performance results show that the proposed framework is robust and can be practically deployed, e.g., inference time less than 800 µs and a marginal rate for the misclassified traffic.

Introduction

The Internet of Things (IoT) has become a powerful and transformative force, driven by the goal to improve safety, efficiency, and sustainability for a wide range of applications. IoT is one of the most promising technological advancements on the horizon is the integration of sixth-generation (6G) networks and the evolution beyond 5G (B5G) as it has proven capacity for innovation and enhancement. This development has the potential to revolutionize communication systems by optimizing processes within smart cities, industries, or powering the seamless operation of intelligent transportation systems (ITS). This also by introducing high speeds, ultra-low latency, and a vastly expanded capacity. These attributes are indispensable for accommodating the expected exponential growth in connectivity. The convergence of the Internet of Things (IoT) with 6G and B5G networks presents an unprecedented opportunity to transform the digital landscape. This technologies integration will enable the deployment of innovative IoT applications that require real-time constraints, reliable communication channels, efficient and predictive resource management, and robust security systems.

In this context, the network communications require "lightweight" protocols for session initiation and control, such as Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP). However, MQTT and CoAP are not applicable in advanced IoT applications that require high data rates and low latency [START_REF] Yang | The implementation of a sip-based service platform for 5g iot applications[END_REF][START_REF] Yang | Aiottalk: A sip-based service platform for heterogeneous artificial intelligence of things applications[END_REF]. Session Initiation Protocol (SIP) is considered as a good candidate, for supporting a wide variety of IoT application scenarios [START_REF] Khalil | Leveraging blockchain for device registration and authentication in tsip-based phone-of-things (pot) systems[END_REF][START_REF] Meshram | An efficient fractional chebyshev chaotic map-based three-factor session initiation protocol for the human-centered iot architecture[END_REF][START_REF] Yang | The implementation of a sip-based service platform for 5g iot applications[END_REF][START_REF] Yang | Aiottalk: A sip-based service platform for heterogeneous artificial intelligence of things applications[END_REF]. Besides instant messaging, it is suitable for handling short or long session semantics for streaming data and publish-subscribe semantics for event notifications, e.g., subscribe, publish, notify or method, thereby enabling more sophisticated IoT network operations. The Session Initiation Protocol (SIP) is well-suited for this purpose and functions as a signaling protocol designed for multimedia communication [START_REF] Mahajan | A deep learning approach to detection and mitigation of distributed denial of service attacks in high availability intelligent transport systems[END_REF][START_REF] Yang | The implementation of a sip-based service platform for 5g iot applications[END_REF][START_REF] Yang | Aiottalk: A sip-based service platform for heterogeneous artificial intelligence of things applications[END_REF]. Nevertheless, one of the major SIP Distributed Denial of Service (DDoS) attacks is the INVITE flooding attack [START_REF] Hussain | A comprehensive study of flooding attack consequences and countermeasures in session initiation protocol (sip)[END_REF]. In this attack, malicious users flood the proxy or SIP server with a high volume of malicious INVITE messages with the aim of disrupting the service.

In this paper, we propose a comprehensive framework specifically designed to detect SIP INVITE flooding attacks. Recent approaches to detecting SIP-DDoS attacks focus mainly on analyzing the content of SIP messages or the entire SIP dialog [START_REF] Nazih | Countering ddos attacks in sip based voip networks using recurrent neural networks[END_REF][START_REF] Pereira | Detection of abnormal sip signaling patterns: A deep learning comparison[END_REF]. However, these works have not been studied to analyze SIP traffic in time intervals. This temporal segmentation makes it possible to systematically examine and evaluate SIP communication patterns, while facilitating timely anomaly detection and improving the overall effectiveness of the IDS. The proposed framework is designed to address the unique challenges posed by such attacks, and to provide an effective defense mechanism against DoS/DDoS threats targeting SIP infrastructures. The core of the proposed framework is a Gated Recurrent Unit (GRU) based Intrusion Detection System (IDS). By harnessing the power of recurrent neural networks, the IDS can effectively process sequential SIP traffic data and identify patterns indicative of INVITE flooding attacks. The GRU's ability to capture temporal dependencies makes it a valuable asset in recognizing attack behaviors with high accuracy. To evaluate the proposed IDS, the Train/Test/Validation protocol was employed, a widely-used technique for assessing the model's performance. Furthermore, an independent test dataset was used to validate the IDS's effectiveness. The results demonstrate the framework's ability to accurately detect and mitigate INVITE flooding attacks, even under different attack intensities, showcasing its robustness and practicality in real-world settings. The proposed framework is available online1 , containing the code, dataset and learning models. The main contributions are the followings:

-Generating a realistic dataset: to ensure the effectiveness of the framework, a diverse and realistic dataset was generated using the SIPp [19] and Mr.SIP [START_REF] Tas | A novel sip based distributed reflection denialof-service attack and an effective defense mechanism[END_REF] The paper is structured as follows. Section 2 reviews the related work, discussing existing research in the field. Section 3 presents the proposed framework, detailing the experimental platform, the dataset generation process, and the approach for detecting SIP DDoS attacks. Section 4 describes the detection methodology, and Section 5 presents the results obtained from deploying the proposed approach. Finally, we conclude by highlighting the key findings and suggesting directions for future research.

Related Works

The related literature shows that DDoS attacks can be considered as one of the most predominant threats in IoT networks [START_REF] Inayat | Learning-based methods for cyber attacks detection in iot systems: A survey on methods, analysis, and future prospects[END_REF][START_REF] Kumari | A comprehensive study of ddos attacks over iot network and their countermeasures[END_REF][START_REF] Mittal | Deep learning approaches for detecting ddos attacks: A systematic review[END_REF][START_REF] Omolara | The internet of things security: A survey encompassing unexplored areas and new insights[END_REF]. DDoS attacks pose significant challenges to the security and stability of IoT infrastructures due to their ability to disrupt services by overwhelming target systems with a massive flow of malicious traffic. This section provides an overview of the proposed dataset for SIP-DoS/DDoS transactions, as well as a review of various studies that address approaches to detecting SIP anomalies in terms of data encoding and technologies used.

SIP dataset

Few datasets have been proposed in the literature for analyzing DDoS attacks. Alvares et al. [START_REF] Alvares | Dataset of attacks on a live enterprise voip network for machine learning based intrusion detection and prevention systems[END_REF] introduce a dataset dedicated to Voice over Internet Protocol (VoIP) networks, while considering various DoS/DDoS attack types, including the INVITE flooding attack. Nassar et al. [START_REF] Nassar | Labeled voip data-set for intrusion detection evaluation[END_REF] present a dataset with multiple SIP-DoS/DDoS attacks, including INVITE flooding. They used "Inviteflood" tool with two intensity scenarios: 100 and 1000 requests per second.

However, these two existing datasets suffer from same limitation or lack of details as they do not provide a comprehensive details regarding the considered specific attack, especially they do not consider the various types of SIP-INVITE flooding attacks and scenarios. Additionally, they do not address the critical issue of invalid IP addresses, in case of IP spoofing attacks. This information can provide a more holistic and realistic representation of practical intrusion scenarios. These shortcomings tend to reduce the ability to properly evaluate IDSs for SIP-INVITE attacks.

SIP anomalies detection

Recent approaches to detecting SIP-DDoS attacks focus mainly on analyzing the content of SIP messages or the entire SIP dialog. Pereira et al. [START_REF] Pereira | Classification of abnormal signaling sip dialogs through deep learning[END_REF] present an IDS model for recognizing SIP signaling patterns to identify abnormal SIP dialogs within observed SIP sequences. Their core approach is based on a LSTM architecture. They compared their methods to a probabilistic-based solution and found that their methods achieved higher detection scores in a shorter time. Extension [START_REF] Pereira | Detection of abnormal sip signaling patterns: A deep learning comparison[END_REF] have been proposed for signaling SIP attacks based on Convolutional Neural Networks (CNN) architecture. The performance evaluation demonstrates that both the CNN and LSTM models achieve similar effectiveness in detecting the most probable SIP dialog identifier. Nazih et al. [START_REF] Nazih | Countering ddos attacks in sip based voip networks using recurrent neural networks[END_REF] introduce an IDS based on the RNN architecture, designed to analyze message content and identify SIP-INVITE flooding attacks. To evaluate the effectiveness of their proposed solution, the authors conduct experiments using a dataset that includes real legitimate SIP messages (normal usage), while the malicious messages (INVITE flooding attacks) are generated using the SIPp-DD tool [START_REF] Stanek | Sipp-dd: Sip ddos flood-attack simulation tool[END_REF]. However, these works have not been studied to analyze SIP traffic in time intervals. This temporal segmentation makes it possible to systematically examine and evaluate SIP communication patterns, while facilitating timely anomaly detection and improving the overall effectiveness of the IDS.

Several SIP message encoding and artificial data generation techniques have been proposed to improve the ability of neural networks to detect DDoS attacks. Nazih et al. [START_REF] Nazih | Countering ddos attacks in sip based voip networks using recurrent neural networks[END_REF] provide a character-or token-based feature extraction process. Raw data is transformed into sequences, following by padding techniques to ensure consistent sequence lengths. Embedding techniques are then employed to represent the data into a format that can be exploited for a meaningful analysis and pattern recognition, related to the SIP-INVITE message. The results clearly demonstrate that the token-based approach combined with the GRU and LSTM architectures give the best performance. Meddahi et al. [START_REF] Meddahi | Sip-gan: Generative adversarial networks for sip traffic generation[END_REF] introduce Generative Adversarial Networks (GAN) to augment SIP messages. The Authors convert the SIP traffic data into images, enabling image-based techniques to be applied to SIP traffic data. The final phase involves the deployment of a GAN model utilized to generate new SIP messages. These newly generated SIP messages are then integrated into the dataset, expanding its size and diversity, effectively. The authors introduce a parameter γ to measure the gap between the synthetic and real SIP-data. Nevertheless, data augmentation using techniques like GAN introduce information loss in the SIP message fields. This limitation is critical for message and traffic analysis in case of intrusion detection. While these techniques have proved effective for detecting SIP-DDoS attacks, focusing on the analysis of SIP message content or the SIP dialog itself, they have not been proven effective for analyzing an attack in real-time sequential SIP traffic data.

Framework

This section, is dedicated to describe the proposed framework. We provide a comprehensive overview of the SIP Protocol, its core principles, and functionalities. We also introduce the tools used in our experimentation, including the simulation environment and data generation techniques. Finally, we outline our SIP dataset, with a focus on the different SIP-INVITE flooding attack scenarios.

SIP protocol overview

The Session Initiation Protocol (SIP) stands as a foundational signaling protocol within the domain of real-time communication [RFC3261-3265], primarily designed to initiate, maintain, modify, and terminate communication sessions between Internet Protocol (IP) devices. SIP finds application in a diverse array of scenarios, encompassing Voice over IP (VoIP), video conferencing, instant messaging, and presence services.

SIP adopts a text-based protocol architecture, following a request-response paradigm. Typically transmitted over User Datagram Protocol (UDP), although Transmission Control Protocol (TCP) is also viable, SIP messages are structured with a header-body format. The header comprises essential information concerning message attributes, source and destination addresses, and various parameters, while the body carries supplementary data, such as session descriptions or multimedia streams.

SIP session establishment is initiated through the transmission of an INVITE message from the caller to the callee. The INVITE message includes a session description, detailing the caller's preferred media types and supported codecs. The callee, upon accepting the session, responds with a 200 OK message, marking the establishment of the session. Subsequently, participants can exchange media streams using the designated codecs.

SIP allows for session modifications and terminations via corresponding SIP messages. To adjust a session, participants may employ a REINVITE message, while session termination is accomplished through a BYE message. The detailed process of the SIP call is illustrated in Fig. 1.

Distinguished by its adaptability and extensibility, SIP incorporates various noteworthy features:

-Scalability: SIP is architected to scale efficiently, accommodating a substantial user base and numerous concurrent sessions; -Reliability: SIP ensures message reliability through mechanisms designed to guarantee successful delivery; -Security: SIP boasts a range of security mechanisms, encompassing authentication and encryption; Widely adopted in the telecommunications sector, the SIP protocol is the backbone of VoIP and real-time communication services offered by many providers. It also finds extensive application in enterprise solutions, including video conferencing and instant messaging.

SIP
Several real-world instances underscore SIP's ubiquity:

-VoIP Calls: SIP underpins VoIP calls via mobile applications, orchestrating call initiation and management; -Video Conferencing: SIP facilitates the establishment and control of video conferences, whether through web browsers or dedicated conferencing clients; -Instant Messaging: In messaging applications, SIP may be leveraged for message delivery; -Presence Services: SIP plays a role in delivering presence information, informing users about the online status of their contacts.

SIP stands as a versatile and influential protocol underpinning a broad spectrum of real-time communication applications, exemplifying its indispensable role within contemporary telecommunications infrastructure.

SIP communication network simulation

The proposed dataset was generated using the SIPp simulator, which served as the User Agent Client (UAC) for generating legitimate INVITE messages, and as the User Agent Server (UAS) for receiving and manipulating SIP call sessions. To simulate the DDoS attack scenarios, the Mr.SIP simulator was utilized as the malicious UAC, generating the INVITE flooding attacks. The network architecture adopted for simulating SIP communication is depicted in Fig. 2, providing an overview of the setup used for the experiments. To ensure the quality and sophistication of the generated data, some modifications have made to the source code of the SIPp simulator. These modifications allowed for the generation of more elaborate SIP INVITE messages, including the main SIP fields such as the source IP address, branch number, URI, and call number. Fig. 3 shows that SIPp is not full compliant with SIP specifications as the fields of the original SIP INVITE message are not randomly generated. This fields are based on a specific format that does not comply with the SIP specifications RFC 3261 [START_REF] Rosenberg | Sip: session initiation protocol[END_REF]. As a result, the generated SIPp messages deviated from the conventional format. The same figure shows an example of the new SIP message generated by SIPp, after the source code modifications were made to enhance and modify the SIP message structure. Furthermore, in addition to generating legitimate INVITE messages, the Mr.SIP tool plays a crucial role in simulating multiple attackers and generating DoS/DDoS attacks through IP-spoofing techniques. The ability to mimic multiple attackers allows us to create realistic and challenging scenarios, simulating the presence of malicious actors attempting to overwhelm the SIP infrastructure with a barrage of malicious INVITE messages.

Training SIP data generation

The INVITE flooding attack is executed by an attacker who generates malicious INVITE messages, by spoofing the IP address and/or URI of legitimate users. The primary objective of this attack is to overwhelm the SIP server by not responding to the SIP 200 message with an ACK SIP message. Consequently, the server enters an ACK wait phase (timeout) while attempting to reestablish communication with the client through a SIP 200 message. The attack's effectiveness lies in the large volume of INVITE messages sent without corresponding ACK messages, leading to a denial of service for the SIP server.

The Mr.SIP tool uses IP spoofing attack to hide the true source of its malicious traffic, making it difficult for the target system to identify the attacker and detect the attack. This enables to evaluate the proposed framework's robustness against sophisticated DDoS attacks that try to obfuscate their origins, mirroring real-world threat scenarios.

In the proposed dataset, the focus is on capturing the root cause of the attacks, which is the excessive volume of INVITE messages without ACK messages. To highlight this aspect, only the SIP transaction of the INVITE message is saved, omitting other related messages. This dataset structure allows for a more specific analysis of the attack pattern. From the literature [START_REF] Hussain | A comprehensive study of flooding attack consequences and countermeasures in session initiation protocol (sip)[END_REF], on the SIP-INVITE flooding rate, the attack can be classified flowing four categories:

Abrupt flooding behavior: the most common type of flooding attack in SIP networks. They occur when the SIP proxy or server receives a large number of requests in a short period of time, in an unusual way. This suggests that the attack does not have a specific rate or pattern.

Very high flooding behavior: the goal of this attack is to overload the system with a large number of INVITE requests in a short space of time. The system may then become unavailable or crash.

Stealthy flooding behavior: designed to evade detection by IDS. Attackers launch stealthy flooding attacks at a slow rate in order to avoid triggering any alarms or alerts. The low rate of this form of attack makes it extremely difficult to distinguish it from regular behavior, and it keeps the flooding rate slightly below the threshold.

Low rate flooding behavior: attacks involving floods can occasionally be launched at a slow rate. The low rate attack has roughly constant flooding rate, unlike stealthy behavior. Giving this, the dataset contains various sequences and scenarios of the IN-VITE flooding attack, as depicted in Fig. 4. Additionally, it includes different sub-classes of attacks, providing further granularity and insights:

A A A A A A A A A A A A (a) (b) (c) (d)
-Very low (VL): 10 messages/sec -Low (L): 25 messages/sec -Medium (M): 50 messages/sec -High (H): 100 messages/sec -Very High (VH): 500 messages/sec -Very High ++ (VH++): 1,000 messages/sec

SIP-INVITE flooding detection

First, the methodology is described, including the sliding window technique and its specific parameters, the datasets distribution and the validation process designed to rigorously evaluate the proposed IDS. In addition, we discuss the selected features for our training model and describe the deep learning algorithms used in our IDS. The effectiveness of our approach is measured, thus the performance metrics we used to evaluate the model's classification of malicious and legitimate SIP-INVITE traffic are described.

Analyze SIP traffic in time intervals

The proposed approach to detecting malicious sequences employs a sliding window technique with specific window size and forward step values. The sliding window size is fixed to 10 messages, corresponds to the "very Low" scenario rate flooding attack, e.g., 10 messages per second. This window size represents the worst case scenario. The assumption is to train the learning model to detect the "worst case" scenario, so that the model is able to detect other, less discrete attack scenarios. The forward step value p between two successive sub-sequences of transmission flows to be analyzed is set at 2, representing a of 2 messages per model inference, thus ensuring overlap between two successive analyses. This sliding window value is justified as the attacker may send only one malicious IN-VITE message to discover and identify the SIP server or proxy. However, once the attacker sends more than one malicious INVITE message, it indicates a potential flooding attack. By using a forward step of 2 messages, the proposed approach can effectively capture and identify the transition from potential to an effective flooding behavior. The Fig. 5 describes the used sliding window technique. Furthermore, regarding the encoding of SIP-INVITE sequences, a sequence is labeled as malicious if it contains a least 2 malicious messages. Otherwise, it is a legitimate sequence. This rule is explicitly outlined in Equation [START_REF] Alvares | Dataset of attacks on a live enterprise voip network for machine learning based intrusion detection and prevention systems[END_REF].

∀ x i , x j ∈ S; i ̸ = j; if ∃ x i = 1 and x j = 1 ⇒ S = 1 (1)
Where "x" represents an INVITE message and "S" is a set of 10 INVITE messages representing SIP-INVITE sequence (S = {x 0 , . . . , x 9 }). Each "x" within the set "S" can take one of two values: "0" signifies a legitimate message, while "1" is a malicious message (x ∈ {0, 1}). By configuring the sliding window with these specific parameters, e.g., 10 messages/sec and forward step 2, we enhance the detection capability of the proposed approach to accurately identify and classify malicious sequences in SIP communication. Indeed, the choice of a step of 2 messages can be seen as a pessimistic approach, as it places us in the worst-case scenario. However, this approach has several advantages. First, it allows us to detect attacks more quickly, the second, it is more robust to scenarios with fewer malicious messages in the sliding window.

Dataset distribution and validation protocol

To evaluate the performance of the proposed IDS system, the Train, Validation and Test protocol was adopted with distinct Train/Validation datasets and two separate Test datasets. This protocol allows to assess the effectiveness of the system across various datasets and ensure its generalisation. The first Test dataset is generated on the same local network used for training. This enables to evaluate the performance of the IDS on typical data, guaranteeing its effectiveness in detecting DDoS attacks in known scenarios. The second Test dataset is generated in a other local network, i.e., the IP address of the SIP client and the SIP server are different from those used in the training phase. This scenario simulates new data (previously unseen), representing real-world type scenarios. Evaluating the IDS on this new data help to asses its generic aspect while detecting DDoS attacks in diverse SIP environments.

The dataset used for training contain 1,135,855 sequences, providing a substantial amount of data for the model. To ensure the effectiveness of the model, a separate validation set was created, consisting of 77,671 sequences. This validation set is used for the evaluation and fine-tuning of the model's performance during the training process. Additionally, a test set comprising 215,455 sequences was generated to assess the final performance and generalisation of the trained model. The test set used in this study is entirely independent of the train and validation datasets to ensure the reliability and generic aspect of the proposed framework.

Table 1 provides the classes distribution in each dataset. This table gives an overview of the distribution of the two different sequences classes (labeled as legitimate and malicious), present in the Training, Validation, and Test datasets. This information is critical for assessing the balance of the two classes within the datasets, which can impact the model's performance and lead to any required adjustments during the training and evaluation phases.

Features selection

Table 2 presents the different main SIP fields, their corresponding data types in the dataset and the selected SIP fields as a features to be used by the model.

- -From (SIP URI of the client): The "From" SIP-field includes the SIP Uniform Resource Identifier (URI) of the client, to identify the sender of a SIP request. This information helps in characterizing the clients involved in the communication and allow to distinguish legitimate clients from malicious attackers. -Call-ID (URI of the call): The Call-ID field contains the URI of the session, providing a unique identifier for each session. This data is used for detecting patterns and correlations between different INVITE messages associated with the same session. -Contact: This field identifies a SIP URI to which a SIP response can be sent.

The SIP contact information can help us to know the client identity.

By selecting these fields as features for the training data, the models can learn and recognize patterns that reflect SIP-DDoS attacks. The combination of timing reception and the selected features (selected SIP fields) enhances the models' ability and effectiveness to differentiate between legitimate and malicious INVITE SIP traffic. The rationale behind excluding certain features lies in their lack of relevance for the considered scenarios. We carefully selected features to streamline the training phase, mitigates the risk of over-fitting and allow the models to focus on the most informative attributes.

Deep learning models

Within the IDS system, the implemented algorithms are: Recurrent Neural Network (RNN) [START_REF] Elman | Finding structure in time[END_REF], Long Short-Term Memory (LSTM) [START_REF] Graves | Long short-term memory. Supervised sequence labelling with recurrent neural networks[END_REF], and Gated Recurrent Unit (GRU) [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF]. These algorithms are good candidates for sequence analysis and have proven to be effective in capturing temporal dependencies of data. By leveraging these algorithms, we can effectively analyze and detect malicious sequences in the SIP communication. The RNN architecture is designed to handle sequential data by processing information in a recurrent manner. LSTM, a variant of RNN, incorporates memory cells to capture long-term dependencies and prevent the vanishing gradient problem. Similarly, GRU also addresses the vanishing gradient problem while maintaining a simpler architecture compared to LSTM. By using RNNs, LSTM and GRU in the IDS-Sequence system, we can leverage their temporal modeling capabilities to capture the temporal properties of SIP data traffic. The pre-training span 10 epochs and used a batch size of 250. We implemented our approach using the PyTorch libraries and as our working environment we used Google Colab with "T4 GPU".

Evaluation metrics

To evaluate the proposed IDS, the following metrics are used: accuracy, precision, recall, and F1-score. These metrics are used for assessing the performance of our detection model for classifying malicious and legitimate SIP-INVITE traffic. In addition to the above metrics and for the sequences based approach, the Intersection over Union metric (IoU) and detection time are also used for the sequences classification. IoU is a typical metric used for tasks related to object detection and segmentation. It measures the overlap between the predicted region and the ground truth region of an object. In the context of sequence classification, IoU can be utilized to determine the similarity between predicted and expected classifications. Detection time is a critical metric used for measuring the time cost and efficiency of the proposed model for detecting malicious SIP-INVITE traffic. It assesses the delay between the time of the received sequence and the time the sequence is correctly classified as malicious or legitimate.

By exploiting these metrics, we provide an extensive and comprehensive performance evaluation for detecting and classifying the malicious and legitimate SIP traffic, with effectiveness.

Where True Positives (TP) correspond to the number of instances detected as true malicious traffic. True Negatives (TN) is the number of instances detected as false malicious traffic. False Positives (FP) is the number of instances the legitimate traffic is detected as a malicious traffic. And, False Negatives (FN) correspond to the number of instances the malicious traffic is detected as a legitimate traffic.

Evaluation

The results in terms of classification, based on the performance metrics, give a high rate for RNN, LSTM, and GRU for each dataset 1 and 2 (Accuracy = 99.9%, Precision = 99.9%, Recall = 99.9%, F1-score = 99.9% and IoU = 99.9%). Also, the performance gap between RNN, LSTM and GRU models is significantly low, with standard deviation of ±0.02. Given this, we propose to focus the analysis based on the comparison of the other metrics: FP, FN and "misclassifed sequence" and "inference time".

The Table 3 presents the results obtained from the RNN, LSTM and GRU models for the two distinct datasets. The evaluation is performed based on misclassified sequences, inference metrics, False Negatives (FN), and False Positives (FP). As shown, GRU outperforms LSTM and RNN models in terms of misclassified sequences. In the dataset 1, the GRU model misclassifies only 63 sequences, demonstrating its superiority over the RNN and the LSTM models, which reach respectively 102 and 109 sequences. For the dataset 2, the GRU model also outperforms LSTM and GRU, with 48 misclassified sequences, while the LSTM misclassifies 85 sequences, and RNN misclassifies 103 sequences. From the inference metrics, the RNN models showcase better inference times. For dataset 1, RNN processes 10 INVITE messages within 700 µs, whereas the GRU model is slightly higher, with a processing time of 763 µs. For dataset 2, RNN shows a lower inference time of 695 µs, while GRU gives 757 µs for the inference time. Regarding the False Positives (FP) parameter, GRU also outperforms RNN and LSTM, showcasing its effectiveness in minimizing FP. For dataset 1, GRU achieves an height performance results, with a False Positive rate of 0.05%, equivalent to the RNN. For dataset 2, GRU maintains its superiority, achieving a False Positive rate of 0.03%. In contrast, RNN reports a slightly higher False Positive rate of 0.05%, while LSTM shows the highest False Positive rate of 0.06%. Regarding the False Negatives (FN) metric, GRU emerges as the front-runner, showcasing its capability to minimize missed classification. For both datasets 1 and 2, GRU achieves a significant low False Negative rate of 0.01%, outperforming RNN by 0.04% while matching the performance of LSTM.

Table 4 provides a detailed and comprehensive illustration regarding the distribution of misclassified sequences within the dataset. It highlights specific sequence patterns that consistently pose challenges for the classification models. These specifics sequences patterns are the most frequently misclassified patterns compared to the others and need further investigations.

In addition, it shows the frequency of misclassifications for GRU, LSTM and RNN. "T-1" and "T-2" in the table indicate their performance for Test datasets 1 and 2. Particularly the sequences (1, 1, 0, 0, 0, 0, 0, 0, 0, 0) and (0, 0, 0, 0, 0, 0, 0, 0, 1, 1) are of particular interest. Indeed, these sequences are somehow challenging for GRU, LSTM and RNN, as they are frequently misclassified. Furthermore, the sequence (0, 0, 0, 0, 0, 0, 0, 0, 1, 0) appears to be challenging for both LSTM and GRU, as they are also misclassified. (1, 0, 0, 0, 0, 0, 0, 0, 0, 0) 500 95 0 7 0 0 21 29 (1, 1, 0, 0, 0, 0, 0, 0, 0, 0) 529 93 31 26 11 8 11 12 (1, 1, 1, 0, 0, 0, 0, 0, 0, 0) 628 110 9 6 0 2 0 0 (1, 1, 1, 1, 0, 0, 0, 0, 0, 0) 1,080 200 8 3 0 0 0 0 (0, 0, 0, 0, 0, 0, 0, 0, 1, 0) 31 7 5 5 7 6 0 0 (0, 0, 0, 0, 0, 0, 0, 0, In conclusion, the performance results highlight that GRU stands out as the most effective and efficient candidate for our classification and use case. Although the GRU model shows the lowest inference times (763 µs for Test dataset 1, 757 µs for Test dataset 2), the gap is not significant compared to the other models (LSTM and RNN) and does not impact the IoT-SIP sessions (few ms to few sec).

Conclusion

We have proposed a framework for detecting and mitigating SIP-INVITE flooding attacks in SIP based IoT infrastructure. We also demonstrate the effectiveness and robustness of our proposed approach through extensive and comprehensive experiments and evaluations under various simulated scenarios. The framework comprises a dataset generated exploiting SIPp and Mr.SIP tools, containing legitimate and malicious INVITE message simulations under various scenarios with different message rates. Scenarios range from 10 messages/s to 1000 messages/s, allowing comprehensive evaluation of the proposed framework. The core component of the proposed framework is a contribution for a GRU-based IDS designed to analyze incoming SIP traffic in real time. GRU constitutes a good candidate for capturing temporal patterns and characteristics in sequential data, while identifying INVITE flooding patterns. By leveraging our datasets for training, the GRU-based IDS is able to distinguish in real time and with high accuracy legitimate and malicious SIP-INVITE messages. The results confirm that the proposed approach and framework can be effectively deployed in SIP based IDS for IoT environments.

For short-term perspectives, we envision two directions: the first one is to investigate the impact of different step values on detection metrics, e.g., performance comparison for step value equal 5 and 10. The second direction is to deploy our proposed solution in real IoT based IDS platform and test-bed.

Fig. 1 .

 1 Fig. 1. An overview of SIP session transaction and an example of SIP-INVITE message

Fig. 2 .

 2 Fig. 2. Architecture of simulated network, where Mr.SIP is used for DDoS attacks traffic generation and SIPp for SIP server and legitimate users

Fig. 3 .

 3 Fig. 3. Changes made to the content of INVITE message fields generated by SIPp to ensure the consistency of the generated data.

Fig. 4 .

 4 Fig. 4. The different generated sequence scenarios of INVITE traffic. (a) legitimate sequence, (b) a sequence that ends with malicious traffic, (c) a sequence that starts malicious traffic, (d) a sequence containing nothing but malicious traffic, (e) a sequence containing legitimate traffic in the middle.

Fig. 5 .

 5 Fig.5. Description of INVITE sequence detection approach: the sliding window size is fixed to 10 messages and the step forward to 2 messages per time unit. If the sequence contains at least 2 malicious messages, it is labeled as malicious. Otherwise, it is a legitimate sequence.

  Time (Temps): The reception time of the INVITE message is an essential feature for identifying the reception time of the sequence associated with potential DDoS attacks. By analyzing the reception times of INVITE messages, security analysts can identify patterns that may indicate a DDoS attack is in progress. For example, if a large number of INVITE messages are received within a short period of time, this may be indicative of a flooding attack. -Source (IP address): This feature indicates the IP address of the client from the sent INVITE message. The source IP address is essential in pinpointing the origin of the message and help to identify suspicious or malicious sources of the DDoS attacks.

Table 4 .

 4 Results for the frequency of pattern classification errors in dataset 1 (T-1) and dataset 2 (T-2).

  tools. This dataset contains a legitimate and malicious INVITE messages with different scenarios and message rates, providing a solid basis for training and evaluating our proposed deep learning based IDS. -Proposing a reliable and reproducible evaluation protocol: a strategy for encoding SIP message frames is proposed to facilitate learning model convergence. Several recurrent neural network models are confronted in order to study their ability to detect anomalies in different attack scenarios. A study of detection accuracy and inference time is carried out.

Original INVITE message from SIPp INVITE message from SIPp

	INVITE sip:service@192.168.56.101:5060 SIP/2.0
	Via: SIP/2.0/UDP 127.0.1.1:5060 ; branch=z9hG4bK-1703-1-0
	From: sipp <sip:sipp@127.0.1.1:5060>;tag=1703SIPpTag001
	To: service <sip:service@192.168.56.101:5060>
	Call-ID: 1-1703@127.0.1.1
	CSeq: 1 INVITE	Original fields contains
	Contact: sip:sipp@127.0.1.1:5060
	Max-Forwards: 70
	Subject: Performance Test
	Content-Type: application/sdp
	Content-Length: 129
	INVITE sip:service@192.168.56.101:5060 SIP/2.0
	Via: SIP/2.0/UDP 192.168.56.106:5060 ; branch=z9hG4bK-572660336-0
	From: User5 <sip:SipPhone5_192.168.56.106@192.168.56.101:5060>;tag=1159126505
	To: service <sip:service@192.168.56.101:5060>
	Call-ID: 10-1827336327@192.168.56.101	New fields contains
	CSeq: 1 INVITE
	Contact: sip:SipPhone5_192.168.56.106@192.168.56.101:5060
	Max-Forwards: 70
	Subject: Performance Test
	Content-Type: application/sdp
	Content-Length: 134

Table 1 .

 1 Data distribution of the datasets used to evaluate the models. Dataset 1 is generated on a unique local network. Dataset 2 corresponds to the same scenarios, but where the SIP client and SIP server IP addresses are different from those used in the training phase.

		Dataset 1 (Training)	Dataset 2 (Inference)
		Train Validation Test	Test
	Legitimate sequence	678,343	44,844	124,019	124,019
	Malicious sequence	457,512	32,827	91,436	91,436
	Total	1,135,855	77,671	215,455	215,455

Table 2 .

 2 Dataset's column and selected features.

	SIP field	Data type	Selected features
	Seq	object	✗
	Temps	object	✓
	Source	object	✓
	MSG	object	✗
	Via	object	✗
	From	object	✓
	To	object	✗
	Call-ID	object	✓
	CSeq	object	✗
	Contact	object	✓
	Max-Forwards	int64	✗
	Subject	object	✗
	Content-Type	object	✗
	Content-Length	int64	✗

Table 3 .

 3 Malicious sequence detection results on the two datasets.

	Dataset 1
		RNN LSTM GRU
	Misclassified sequences	102	109	63
	Inference Time (10 messages) 700.00 µs 749.00 µs 763.00 µs
	FP	0.05 %	0.1 %	0.05 %
	FN	0.04 % 0.01 % 0.01 %
	Dataset 2
		RNN LSTM GRU
	Misclassified sequences	103	85	48
	Inference Time (10 messages) 695.00 µs 753.00 µs 757.00 µs
	FP	0.05 % 0.06 % 0.03 %
	FN	0.04 % 0.01 % 0.01 %

Repository: https://gvipers.imt-nord-europe.fr/benjamin.allaert/sip-ddos

Acknowledgment

This work has been carried in the context of the project Beyond5G, funded by the French government as part of the economic recovery plan, namely "France Relance" and the investments for the future program.