Luca Galbusera

Georgios Giannopoulos

EXPLOITING WEB ONTOLOGIES FOR AUTOMATED CRITICAL INFRASTRUCTURE DATA RETRIEVAL

Keywords: Critical infrastructures, web ontologies, open data, OpenStreetMap

Semantic web technologies play a significant role in many open data initiatives, including geo-mapping projects and platforms. At the same time, semantic principles are also promoted as a key enabling factor for multi-domain analyses of critical infrastructures as well as for improved emergency response. This chapter reviews the recent literature on ontology-based analysis and management of critical infrastructures, and proposes the use of ontology processing techniques to bridge the gap between infrastructure knowledge representation and available (often general-purpose) open data sources. In particular, it discusses an approach for matching a given critical infrastructure ontology to an ontology built on OpenStreetMap (OSM) tags that enables structured access to the associated geographical dataset.

Introduction

Directive 2003/98/EC [START_REF]European Parliament and Council of the European Union[END_REF] and revised Directive 2013/37/EU [START_REF]European Parliament and Council of the European Union[END_REF] are fundamental policy references for the implementation of European Union initiatives on public data sharing. Furthermore, Commission Decision 2011/833/EU [18] enforces the principles of extended accessibility, economy of access to data, reusability and expanded data reach. Accordingly, the European Union Open Data Portal [START_REF]EU Open Data Portal[END_REF] was established as "the single point of access to a growing range of data from the institutions and other bodies of the European Union (EU)." The portal offers searching, exploration and downloading functionalities, and supports semantic technologies (e.g., SPARQL queries) based on linked data principles. Data is free for use and reuse for commercial and non-commercial purposes, and users can provide suggestions and feedback.

Directive 2007/2/EC [21] established the Infrastructure for Spatial Information in the European Community (INSPIRE), which promotes extensive geographic data sharing. The data infrastructure under development [START_REF]Infrastructure for Spatial Information in Europe, INSPIRE[END_REF] has the objective to support European Union environmental and environment-related policies and activities by enabling data sharing and access by public organizations and the community. A series of implementation stages is planned for completion by 2021 [START_REF]Infrastructure for Spatial Information in Europe, Roadmap[END_REF]. Related legislation [START_REF]Infrastructure for Spatial Information in Europe, Legislation[END_REF] focuses on data specifications, metadata, network services, data and service sharing, monitoring and reporting.

Several other governmental and non-governmental open data initiatives are also in place [START_REF] Nicol | Open Data Access Policies and Strategies in the European Research Area and Beyond[END_REF]. An interesting phenomenon is represented by the surge of voluntary open data projects, many of which focus on geographic knowledge. Ballatore et al. [START_REF] Ballatore | A survey of volunteered open geo-knowledge bases in the semantic web[END_REF] provide an overview of a number of these efforts, mainly focusing on global-scoped and mostly crowdsourced projects based on popular semantic web formats specific to geographic data and to broader knowledge realms.

Over time, the original concepts informing the creation of the first geographic portals have been evolving towards a next-generation vision involving "multiple connected infrastructures based on open access and participation across multiple technological platforms that will address the needs of different audiences" [START_REF] Goodchild | Next-generation Digital Earth[END_REF]. Additionally, Digital Earth [START_REF] Gore | Earth in the Balance: Ecology and the Human Spirit[END_REF] is expected to evolve into "a digital nervous system of the globe, actively informing about events happening on (or close to) the Earth's surface by connecting to sensor networks and situation-aware systems" [START_REF] Goodchild | Next-generation Digital Earth[END_REF]. Semantic heterogeneity is acknowledged as one of the key challenges to achieving this objective. Semantic web principles are being applied to address this issue and the Semantic Geospatial Web initiative, supported by the W3C Geospatial Semantic Web Community Group [START_REF]World Wide Web Consortium, Geospatial Semantic Web Community Group[END_REF], promotes the use of geospatial ontologies, semantic gazetteers and geographic vocabularies.

Semantic-web-oriented effort are accompanying the development of Open-StreetMap (OSM) [START_REF]OpenStreetMap[END_REF], a collaborative mapping project that aggregates geographic information collected on a voluntary basis. OpenStreetMap data is organized into different element types and users complement them with freelychosen tags in order to associate meaning and supporting information with geographic items. Each tag is defined by a (key, value) pair and, while the use of existing tags is encouraged, contributors are allowed to introduce new tags.

As a result of this intrinsic flexibility, the set of tags in use dynamically evolves in time along with geographic entries. Therefore, in addition to the tag reference pages provided by the OpenStreetMap Wiki [START_REF]OpenStreetMap Wiki[END_REF], projects such as Taginfo [START_REF]OpenStreetMap Taginfo[END_REF] exist to keep track of the tags currently represented in OpenStreetMap and to provide statistics about their usage. Methods and tools based on semantic web principles have been proposed to overcome tag heterogeneity and enable structured access to the OpenStreetMap dataset and related resources. The LinkedGeoData portal [START_REF] Linkedgeodata | LinkedGeoData -Adding a Spatial Dimension to the Web of Data[END_REF] accommodates OpenStreetMap-sourced dataset information and links to third-party projects in a semantic web format [START_REF] Auer | LinkedGeoData: Adding a spatial dimension to the web of data[END_REF][START_REF] Stadler | LinkedGeoData: A core for a web of spatial open data[END_REF].

A resource description framework (RDF) graph representation based on the OpenStreetMap Wiki contents is provided by the OpenStreetMap Semantic Network [START_REF]OpenStreetMap Foundation, OpenStreetMap Semantic Network, Sutton[END_REF] that also maps OpenStreetMap tags to corresponding concepts in the WordNet lexical database [START_REF]WordNet: A Lexical Database for English[END_REF] and LinkedGeoData [START_REF] Ballatore | A survey of volunteered open geo-knowledge bases in the semantic web[END_REF]. An ontology constructed over the set of OpenStreetMap tags is provided by the OSMonto Project [50], enabling hierarchically structured access to tags and providing a baseline reference to interface with other types of ontologies in order to perform, for instance, semantic analysis [START_REF] Codescu | OSMonto -An ontology of OpenStreetMap tags[END_REF].

These efforts are part of a wide landscape of general ontology application scenarios. Uschold and Gruninger [START_REF] Uschold | Ontologies and semantics for seamless connectivity[END_REF] identify the following four categories:

Neutral Authoring: Information artifacts are authored in a unified, ontology-based language, supporting conversions to multiple target formats and ultimately overcoming interoperability constraints imposed by ad hoc approaches.

Common Access to Information: Ontologies are exploited to translate information between various formats and representations.

Ontology-Based Specification: Ontologies provide a basis for specifying and developing applications.

Ontology-Based Search: Ontologies are used to support structured access to information repositories, enabling their organization and classification at appropriate levels of abstraction.

Ontologies can be expressed using formal languages, often based on firstorder logic or descriptive logic. Researchers [START_REF] Corcho | A roadmap for ontology specification languages[END_REF][START_REF] Gomez-Perez | Ontology languages for the semantic web[END_REF] have examined the development of ontology languages, in general, and web ontology languages, in particular. The OWL Web Ontology Language is one of the most common standards in use today [START_REF] Antoniou | A Semantic Web Primer[END_REF].

As discussed in this chapter, ontologies and semantic (web) technologies are also being promoted in the literature to support the analysis and management of critical infrastructures. For instance, they help provide a systematic representation of heterogeneous systems in terms of entities and their interconnections for study and simulation purposes. They can also portray threat types, targets and actors involved in disaster response, as well as enhance emergency management and information sharing [START_REF] Liu | Ontologies for crisis management: A review of the state of the art in ontology design and usability[END_REF]. The emergence of semantic-oriented approaches in the geospatial information and critical infrastructure protection communities provide opportunities to create enhanced critical infrastructure analysis and management solutions. Indeed, geo-information sources such as OpenStreetMap contain valuable critical infrastructure information and they stimulate community efforts to overcome crises [START_REF] Herfort | Towards assessing the quality of volunteered geographic information from OpenStreetMap for identifying critical infrastructures[END_REF][START_REF] Soden | From crowdsourced mapping to community mapping: The post-earthquake work of OpenStreetMap Haiti[END_REF].

This chapter considers all the aspects discussed above and relates the use of ontological representations of critical infrastructure concepts (e.g., assets, threats and interdependencies) to information collection from data sources. Several ontology-based methods for critical infrastructure analysis and management are reviewed, and a method for critical infrastructure information retrieval from open data sources with an emphasis on OpenStreetMap is proposed. The method exploits ontology mapping as a means to interface an assumed ontology describing a critical infrastructure system to a second ontology constructed on the OpenStreetMap tag system.

Ontological Approaches

Ontologies play a significant role in the development of models of critical infrastructures and their management during and after adverse events. This section reviews recent literature in the areas of conceptual modeling of critical infrastructures, critical infrastructure simulation and information sharing.

Critical Infrastructure Modeling

Systematizing knowledge about critical infrastructures requires the establishment of a consistent semantics and a means for addressing the diversity that stems from an inherently multi-disciplinary investigation area. At the same time, it opens many opportunities for analysis, including automated reasoning and decision support. The construction of taxonomies is a fundamental step in this direction. Drawing on the work by Perrow [START_REF] Perrow | Normal Accidents: Living with High-Risk Technologies[END_REF], Rinaldi et al. [START_REF] Rinaldi | Identifying, understanding and analyzing critical infrastructure interdependencies[END_REF] have proposed a seminal taxonomy of critical infrastructure elements and their interdependencies. Another notable work is the Infrastructure Data Taxonomy of the U.S. Department of Homeland Security [START_REF]Infrastructure Data Taxonomy[END_REF] that has been used to guide and structure analyses [START_REF] Tolk | The next generation of modeling and simulation: Integrating big data and deep learning[END_REF]. Taxonomies have also been employed to categorize critical infrastructure threats and attacks (see, e.g., [START_REF] Kozik | Current cyber security threats and challenges in critical infrastructure protection[END_REF] for cyber-related threats).

Wolthusen [START_REF] Wolthusen | Modeling critical infrastructure requirements[END_REF] has proposed a method for representing critical infrastructure systems that is oriented towards data collection and exchange as well as modeling and simulation. The approach starts with a high level description of entities and dependencies, and exploits multigraphs to handle different types of dependencies. An ontological model and exchange mechanism data format are introduced based on RDF and OWL, and a multi-domain critical infrastructure representation sourced from expert knowledge is formalized.

Lee and Gandhi [START_REF] Lee | Ontology-based active requirements engineering framework[END_REF] have introduced an ontology-based active requirements engineering framework for software-intensive systems analysis based on a hierarchical representation that includes top-level generic requirements, mid-level domain spanning requirements and leaf-node subdomain requirements.

Sotoodeh and Kruchten [START_REF] Sotoodeh | An ontological approach to conceptual modeling of disaster management[END_REF] present a conceptual modeling framework for disaster management that comprises three ontologies: an emergency operation center ontology related to disaster response components and two disaster affecting infrastructure ontologies that represent infrastructures and their reference communities along with their relationships at a high level of abstraction. Concepts such as regions and people with associated wellness characterizations are included together with infrastructure and resource characterizations. Interdependencies are described at the physical and social levels.

Sicilia and Santos [START_REF] Sicilia | Main elements of a basic ontology of infrastructure interdependency for the assessment of incidents[END_REF] have introduced an infrastructure incident assessment ontology for the high-level representation of infrastructures, incidents and their causes. It involves a service-dependent semantics of connections. Interdependencies (physical, connectivity-based, policy-based and procedure-based) can be specified a priori or inferred using Semantic Web Rule Language (SWRL) rules. The use of reasoning techniques to support emergency response is also demonstrated.

A network security framework has been developed by the INTERSECTION Project [11] for identifying and classifying vulnerabilities in heterogeneous networks [START_REF] Choras | Ontology-based decision support for security management in heterogeneous networks[END_REF]. The framework comprises an ontology that extends beyond singledomain networks and focuses on resources and vulnerabilities as the key components. Four resource subclasses are identified: (i) physical resources; (ii) logical resources; (iii) software; and (iv) services. In addition, three vulnerability subclasses are identified: (i) physical resource vulnerabilities; (ii) logical resource vulnerabilities; and (iii) software vulnerabilities. An OWL-based decision support architecture is presented as well.

An ontology handling tool created by the INSPIRE Project [10] and described in [START_REF] Bouet | INSPIRE Ontology Handler: Automatically building and managing a knowledge base for critical information infrastructure protection[END_REF] is a standards-based instrument for enabling automatic audits of the security and criticality levels associated with information systems. Its infrastructure discovery component, ontology repository and expert visualization tool combine to facilitate analyses of critical infrastructure vulnerabilities while considering the associated information and communications technology components.

Creese et al. [START_REF] Creese | A logical high-level framework for critical infrastructure resilience and risk assessment[END_REF] have used automated reasoning for critical infrastructure resilience assessment problems based on a top-down, layered conceptual mapping of assets, controls, vulnerabilities and risk. An ad hoc dependency modeling language that exploits a natural-language-like semantics is used to enable automated reasoning and what-if analyses with a focus on organizational aspects.

El-Diraby and Osman [START_REF] El-Diraby | A domain ontology for construction concepts in urban infrastructure products[END_REF] have used domain ontologies to depict urban infrastructures in terms of processes, actors and products. Physical products are classified into generic and sector-specific products. Infrastructure products are characterized in terms of the functions they perform (i.e., conveyance, control, protection, access, measuring, storage and locating products). The composition of products is allowed and an extended set of attributes (i.e., dimensional, spatial, material, shape, cost, performance, surrounding soil, dependency, redundancy and state of operation attributes) is specified for each product. The construction of the ontology is subject to formal consistency checks and an expert elicitation-based assessment.

De Nicola et al. [START_REF] De Nicola | A lateral thinking framework for semantic modeling of emergencies in smart cities[END_REF] have presented an ontology and semantic rules for emergency management in smart cities. CEML is used as the reference modeling language. The knowledge modeling strategy employs an upper-level ontology that extends the domain ontology with CEML concepts, along with an emergency ontology that enables automated knowledge management and reasoning. A tool exploiting SPARQL provides automated support for defining emergency management plans.

Xu et al. [START_REF] Xu | Modeling and representation for earthquake emergency response knowledge: Perspective for working with geo-ontology[END_REF] employ geo-ontologies for earthquake emergency response. Knowledge is organized into four classes: (i) factual knowledge; (ii) rule-based knowledge; (iii) procedural knowledge; and (iv) meta-knowledge. The content types include emergency response and rescue knowledge, disaster information estimation, emergency information and terms, and emergency foundation data.

Takahashi and Kadobayashi [START_REF] Takahashi | Reference ontology for cybersecurity operational information[END_REF] provide a list of industry specifications and a reference ontology for cyber security operational information. Conceptual models that target cyber dependencies are considered in [START_REF] Oltramari | Towards a human factors ontology for cyber security[END_REF], where a human factors ontology is employed to specify a cyber security framework. Other researchers [START_REF] Trucco | An ontology-based approach to vulnerability and interdependency modeling for critical infrastructure systems[END_REF][START_REF] Trucco | Ontology-based approach to disruption scenario generation for critical infrastructure systems[END_REF] discuss an ontology-based approach for vulnerability and interdependency representation as well as disruption scenario generation for critical infrastructures. Luo et al. [START_REF] Luo | Application of ontology in emergency plan management of metro operations[END_REF] have developed a knowledge modeling formalism for emergency situations and planning in metropolitan areas, and have implemented it in a training tool.

Critical Infrastructure Simulation

Ontologies are widely used in critical infrastructure simulation architectures. In particular, they provide model specifications and enable the integration and combination of multiple analysis techniques and tools. Conceptual interoperability has been formally studied in the context of simulation theory. Wang et al. [START_REF] Wang | The levels of conceptual interoperability model: Applying systems engineering principles to M&S[END_REF] have introduced a conceptual interoperability model for determining the degree of interoperability of a system. Various aspects of integration, interoperability, composability are discussed in [START_REF] Page | Toward a family of maturity models for the simulation interconnection problem[END_REF].

Critical infrastructure taxonomies are leveraged in creating complex critical infrastructure simulators. For example, Tolk et al. [START_REF] Tolk | Reference modeling in support of M&S -Foundations and applications[END_REF] have presented a modeling and simulation development framework, and have used it in a case study involving the Infrastructure Data Taxonomy of the U.S. Department of Homeland Security [START_REF]Infrastructure Data Taxonomy[END_REF].

Van Dam and Lukszo [START_REF] Van Dam | Modeling energy and transport infrastructures as a multi-agent system using a generic ontology[END_REF] have employed agent-based models for the energy and transportation infrastructures. Their bottom-up methodology, which is motivated by the presence of multiple decision makers, distributed nature of the problem and dynamic operational environments, involves a generic ontology that is customized to the domains of interest based on expert opinion.

The IRRIIS Project [12] has developed an ontological information model for vulnerability analyses of large and complex critical infrastructures [START_REF] Klein | Information modeling and simulation in large dependent critical infrastructures -An overview of the European Integrated Project IRRIIS[END_REF]. It is implemented in a federated simulation environment and supports the development of a risk estimator for determining if specific conditions in an infrastructure are critical singly or in combination.

The DIESIS Project [9] has adopted a layered approach for federated critical infrastructure simulation. It employs ontologies to describe the dynamic bindings between subsystems [START_REF] Rome | DIESIS: An interoperable European federated simulation network for critical infrastructures[END_REF]. An ontology component is used to express meta-knowledge (abstract representations of basic system concepts and relationships); this is accompanied by an infrastructure ontology (domainspecific critical infrastructure ontology) and a federation ontology (for specifying semantically-coherent interconnections and rules).

Masucci et al. [START_REF] Masucci | Ontologybased critical infrastructure modeling and simulation[END_REF] discuss the derivation of ontology components and their relationships using OWL and SWRL. Castorini et al. [START_REF] Castorini | Ontological framework to model critical infrastructures and their interdependencies[END_REF] describe an application involving the power grid, railway and telecommunication domains, and their mutual relationships. Interested readers are referred to [START_REF] Tofani | An ontological approach to simulate critical infrastructures[END_REF][START_REF] Usov | The DIESIS approach to semantically interoperable federated critical infrastructure simulation[END_REF] for more details.

The I2Sim interdependency simulator is a key contribution to ontology-based simulations of critical infrastructures [START_REF] Marti | Dynamic recovery of critical infrastructures: Real-time temporal coordination[END_REF]. To support I2Sim development, an ontology is presented for modeling temporal dependencies between infrastructures based on tokens (goods and services provided by one entity to another), cells (entities that perform functions), nodes (token generators) and transportation channels (flows of tokens subject to capacity and time delay constraints).

Ventura et al. [START_REF] Ventura | Understanding interdependencies among critical infrastructures[END_REF] expand on this approach and introduce a taxonomy for classifying infrastructure interdependencies based on criteria such as the nature of the involved entities (human-object, object-object or human-human), directions of relationships (unidirectional or bidirectional), nature of relationships (information, physical, geographical or organizational/human/societal), states of relationships (static or dynamic) and type of failure if disrupted (cascading failure in associated entities, escalating failure or common origin failure).

Grolinger et al. [START_REF] Grolinger | Federated critical infrastructure simulators: Towards ontologies for support of collaboration[END_REF] explore ontologies associated with a water distribution simulator and power system simulator, and map them to the I2Sim ontology. According to Grolinger et al., while federated simulation approaches as used in the DIESIS Project attempt to "integrate existing domain simulators by enabling their coordination and collaboration," I2Sim belongs to the architectural type that "includes simulation frameworks that enable the modeling of different infrastructures and their interdependencies."

Information Sharing

Another research topic covers information sharing and the related interoperability concerns, especially the need to manage the diversity of data sources and formats in emergency response procedures that often involve a number of actors. The literature in this field is extensive and has strong relationships with the conceptual modeling and simulation of critical infrastructures. Some of the literature discussed above addresses this aspect as well. However, certain recent contributions related to information sharing remain to be discussed. Kim et al. [33] present an information sharing mechanism based on ontologies that addresses cyber dependencies between infrastructures. Di Maio [START_REF] Maio | An Open Ontology for Open Source Emergency Response System[END_REF] has proposed an open ontology approach that improves the performance of emergency response systems based on the principle of collaboration. Di Maio also discusses different levels of conceptual interoperability and the important notion of resilience in emergency response systems.

The interoperability gap affecting emergency planning systems is addressed in [START_REF] Wang | Ontology modeling of emergency plan systems[END_REF] using an emergency planning ontology. This ontology, which is based on the suggested upper merged ontology, is formally specified in terms of concepts, relations, functions, axioms and instances.

Galton and Worboys [START_REF] Galton | An ontology of information for emergency management[END_REF] have proposed architectural specifications for interoperability that take into account sensor networks and crowdsourced informa-tion collection procedures, together with related spatio-temporal data distribution considerations. Galton and Worboys also note that open-source geospatial information can be very useful in an emergency management framework.

Li et al. [START_REF] Li | Community-based cloud for emergency management[END_REF] describe a cloud computing platform for emergency management that relies on crowdsourced information. Drawing on existing emergency management ontologies, Li et al. introduce a novel ontology that considers additional information such as the types of hazards and emergencies, as well as meteorological factors. Castrucci et al. [START_REF] Castrucci | Design and implementation of a mediation system enabling secure communications among critical infrastructures[END_REF] have developed a mediation system that enables secure communications between critical infrastructures, implements fault mitigation strategies and supports information discovery while overcoming information exchange and data heterogeneity problems.

Ontology-Based Information Retrieval

The literature review in the previous section covers conceptualizations of various aspects of critical infrastructure protection and emergency management. An important point is that structured descriptions of critical infrastructures can also enhance information retrieval processes. As a consequence, this section focuses on an information retrieval approach for critical infrastructures based on ontology matching or alignment [START_REF] Euzenat | Ontology Matching[END_REF][START_REF] Shvaiko | Ontology matching: State of the art and future challenges[END_REF].

Ontology matching techniques seek to find relationships between elements of different ontologies under analysis and provide similarity measures. An automated procedure for performing the alignment is important when dealing with large ontologies, multi-step information exchange chains and real-time processing. The complexity of multiple ontology matching applications can be managed by combining the alignment criteria and the resulting similarity measures using expert judgment or artificial intelligence [START_REF] Vazquez Naya | Improving ontology alignment through genetic algorithms[END_REF].

The proposed matching-based approach has three sub-tasks: (i) ontology population; (ii) ontology matching; and (iii) ontology-driven data retrieval. These sub-tasks are described below.

Ontology Population

In the first step, two starting ontologies CI Ont and T Ont are created and populated. CI Ont is a domain ontology that describes a set of critical infrastructure components, threats and their relationships. T Ont is a target ontology built on a reference dataset from which information is to be retrieved. The application focuses on the OpenStreetMap geographic dataset.

The following are the key aspects involved in constructing the two ontologies:

CI Ont: An OWL ontology CI Ont is constructed to include classes that describe the set of critical infrastructure sectors, sub-sectors and asset types (i.e., critical infrastructure classes) relevant to the geographic information retrieval problem and classes that describe the set of threats (threat classes). Subclass relationships are established between the elements of the critical infrastructure classes based on general-purpose and specialized glossaries and taxonomies. Interdependency relationships are also specified between critical infrastructure classes based on information collected from the technical literature. Furthermore, threat-to-criticalinfrastructure class relationships are specified to express the significance of threats to the various infrastructure elements.

T Ont: The T Ont ontology is populated with tag information via a semi-automated process based on the Taginfo system. A set of significant reference keys (e.g., building, highway, natural, land use, surface, power, waterway, wall, amenity, leisure, railway) is first identified for the analysis domain of interest and consistent with CI Ont. A set of values is extracted for each key from the same source based on a filtering criterion. As in the case of the OSMonto ontology, the filtering criterion only includes values used a sufficiently high number of times according to the statistics provided by Taginfo. The resulting keys and values are arranged into the OWL ontology T Ont to express the hierarchical relationships between keys and their associated values.

Ontology Matching

An alignment is computed based on the CI Ont and T Ont ontologies and additional lexical resources. A preprocessing step and a multi-step alignment procedure are involved.

Preprocessing. In this step, the CI Ont and T Ont ontologies are processed to adhere to established orthographic rules and standards (e.g., hyphenation and capitalization of labels). Furthermore, CI Ont undergoes an ontology enrichment stage. This is accomplished by defining a lexicon L CI Ont by collecting the labels associated with the CI Ont classes. The lexicon is partitioned into L CI CI Ont and L th CI Ont by collecting the labels of the critical infrastructure and threat classes, respectively.

For each element in L CI CI Ont , a set of relevant synonyms is fetched from the WordNet database via an automated routine that uses the MIT Java Wordnet Interface [43]. Correspondingly, the enriched critical infrastructure ontology CI Ont,e is constructed by extending CI Ont with the synonym entries and establishing consistent equivalence relationships. The associated enriched lexicon L CI Ont,e collects the labels of the extended set of classes so that L CI Ont ⊆ L CI Ont,e . The lexicon L CI Ont,e is partitioned into L CI CI Ont,e and L th CI Ont , where L CI CI Ont,e collects the extended set of labels of the critical infrastructure classes in the enriched ontology. Finally, in the case of T Ont, a lexicon L T Ont is created based on OpenStreetMap keys and values expressed in the ontology.

Multi-Step Alignment. The alignment procedure involves the following steps that are performed in sequence: Lexical Matching: Exact element matches between L CI CI Ont,e and L T Ont are determined.

String Matching: String similarity metrics are employed to compare terms from the two lexicons. One of the metrics used is the Levenshtein distance (e.g., [START_REF] Stoilos | A string metric for ontology alignment[END_REF]).

The two matching methods are applied sequentially so that the alignments found via lexical matching are included directly in the final result while string matching is used to search for additional relevant bindings. The final alignment is produced using a similarity aggregation criterion encoded in a matching matrix M(L CI CI Ont,e , L T Ont ) ∈ [0, 1] (|L CI CI Ont,e |,|LT Ont |) based on similarity thresholding. To increase the confidence levels when applying string matching, it is possible to consider the presence of multiple matches among CI Ont synonym classes and T Ont components that are associated with OpenStreetMap tag values that refer to the same keys.

Data Retrieval

The inclusion of threat classes, threat-to-critical-infrastructure class relationships and interdependency relationships in CI Ont enables the discovered alignment to be used for targeted data extraction. This is accomplished by defining a relationship map in terms of the following adjacency matrices: |) for all threat-to-critical-infrastructure class relationships, where (i, j) = 1 means that threat i affects critical infrastructure component j based on lexical indexing. The three adjacency matrices are then combined with the matching matrix described above. The set of T Ont items of interest is then obtained starting from each considered threat component. Thus, starting from a specified threat scenario and a geographical area of interest, it is possible to use the composition and query OpenStreetMap based on the significant (key, value) items that are found. The operation can be performed, for instance, by using the Overpass API [START_REF]Overpass API[END_REF] via the Overpass Turbo interface (see overpass-turbo.eu).

A th→CI ∈ {0, 1} (|L th CI Ont |,|L CI CI Ont
As an example, consider a scenario where waterways are affected by a specified threat and the road infrastructure may be affected due to the interdependencies. For both components, the matcher identifies a set of relevant (key, value) pairs to include in a query, whose output is presented in Figure 1. The identified pairs include several facilities that are related to the waterway and road sectors.

Conclusions

This research has focused on the use of semantic technologies in critical infrastructure analysis with an emphasis on information retrieval. Of particular interest has been the use of ontologies for critical infrastructure modeling and simulation as well as for emergency response. The proposed approach for ontology-based information retrieval from open geographic data sources (especially, OpenStreetMap) is applicable to critical infrastructure protection. The target ontology describing the OpenStreetMap content is constructed based on statistics about the actual use of tags, which evolves over time. An ontology specified for critical infrastructures incorporates threat and interdependency information and is enriched during processing. The matching procedure used for querying information is layered in terms of alignment methods and comprises lexical and string matching components. The principal contribution of this research is its ability to foster critical infrastructure tool integration, interoperability and composability. Moreover, the structured access to opensource information facilitated by the proposed approach can enhance multisector knowledge advancement, especially in conjunction with expertise from various technical fields.

Future research will incorporate interactive matching and special taxonomies and dictionaries devoted to critical infrastructures for ontology enrichment and improved similarity aggregation. Additionally, efforts will focus on inference mechanisms for incrementally improving the reference critical infrastructure ontology and on incorporating data quality checking mechanisms.

A

  CI→CI ∈ {0, 1} (|L CI CI Ont |,|L CI CI Ont |) for all critical infrastructure interdependency relationships. A CI→CI,e ∈ {0, 1} (|L CI CI Ont |,|L CI CI Ont,e |) for all synonym relationships.

Figure 1 .

 1 Figure 1. Query outputs for waterway (left) and road infrastructures (right).


Mark Yampolskiy

Wayne King

Gregory Pope

Sofia Belikovetsky

Yuval Elovici

Evaluation of additive and subtractive manufacturing from the security perspective

Keywords: Additive manufacturing, subtractive manufacturing, attack framework

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The U.S. Department of Homeland Security has designated manufacturing as one of the sixteen critical infrastructure sectors [START_REF]Critical Infrastructure Sectors[END_REF]. The critical infrastructure sectors are not isolated, but are entangled in a complex web of dependencies and interdependencies [START_REF] Rinaldi | Identifying, understanding and analyzing critical infrastructure interdependencies[END_REF]. A large-scale critical infrastructure disruption poses technical as well as economic, geopolitical, environmental and societal risks [START_REF] Helbing | Globally networked risks and how to respond[END_REF]. As a result, maintaining the security of manufacturing systems is of paramount importance.

Since the introduction of computer numeric control (CNC) machines in the 1940s, manufacturing processes have become increasingly computerized. Computer numeric control machines enable software control of cutting tools -they reduce a solid block of source material to a desired shape in a process commonly referred to as subtractive manufacturing (SM). In the 1980s, additive manufacturing (AM) was introduced -it is an alternative process in which thin layers of source material are deposited and fused together to form a 3D object. Additive manufacturing machines are often referred to as 3D printers. Subtractive and additive manufacturing machines, which are examples of cyber-physical systems (CPSs), are employed in computer-aided manufacturing (CAM), a process in which manufacturing as well as source material loading and physical transportation are computerized.

Subtractive manufacturing has dominated the manufacturing industry for decades and, until recently, additive manufacturing was predominantly used to produce low-quality plastic parts. However, additive manufacturing technologies have now matured to produce high-quality plastic and metal 3D-printed objects that are usable as functional parts, even in safety-critical systems such as jet engines. Additive manufacturing with other source materials, including ceramics, glass and composites, is rapidly approaching the maturity needed for industrial applications.

According to the 2015 Wohlers Report [START_REF] Associates | Wohlers Report[END_REF], the additive manufacturing industry had °5.165 billion in revenue with 32.5% of all manufactured objects used as functional parts. An Ernst & Young study [START_REF] Muller | How Will 3D Printing Make Your Company the Strongest Link in the Value Chain? EY's Global 3D Printing Report[END_REF] reports that additive manufacturing technologies are being rapidly adopted around the world. In the United States, 16% of the surveyed companies had experience with additive manufacturing and another 16% were considering adopting the technology. The current world leader in additive manufacturing adoption is Germany with 37% of the surveyed companies already employing additive manufacturing and another 12% considering the technology. Numerous studies indicate that the adoption of additive manufacturing will continue to rise, potentially leading to its dominance as the manufacturing technology of the future.

The growing importance of additive manufacturing and its reliance on computerization have led several researchers to voice security concerns [START_REF] Faruque | Acoustic side-channel attacks on additive manufacturing systems[END_REF][START_REF] Brown | Legal aspects of protecting intellectual property in additive manufacturing[END_REF][START_REF] Turner | Bad parts: Are our manufacturing systems at risk of silent cyberattacks[END_REF][START_REF] Yampolskiy | Intellectual property protection in additive layer manufacturing: Requirements for secure outsourcing[END_REF][START_REF] Yampolskiy | Towards security of additive layer manufacturing[END_REF][START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF][START_REF] Yampolskiy | Using 3D printers as weapons[END_REF][START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF]. However, it is not clear whether and how additive manufacturing security differs from the security of other cyber-physical systems, especially as traditional subtractive manufacturing serves the same purpose and employs similar processes and computerized components. This chapter attempts to an-swer these questions and identify the similarities and differences between additive and subtractive manufacturing technologies from the security perspective, but the emphasis remains on additive manufacturing.

Related Work

Additive and subtractive manufacturing systems are both cyber-physical systems. Therefore, the fundamental aspects of cyber-physical system attacks are applicable to both types of systems. Industrial control systems are also computer-controlled systems, but their mission and implementation differ significantly from additive and subtractive manufacturing systems.

No information is available about attacks that have specifically targeted subtractive manufacturing systems. However, there is a growing body of literature on potential attacks on or using additive manufacturing systems. Therefore, this topic is discussed in this section.

Cyber-Physical System Security

Cyber-physical systems generally employ closed or open control loops. A closed-loop system directly uses feedback information from sensors for decision making while an open-loop system makes decisions based on a model of a controlled physical process or the input of a human operator who makes decisions based on sensor readings. In a control system, information from sensors is fed to a computing unit that chooses the necessary actions; the signals from the computing unit are sent to actuators. As discussed in [START_REF] Cardenas | Secure control: Towards survivable cyber-physical systems[END_REF], a cyber-physical system can be attacked by interrupting one of these communications links or by injecting incorrect sensor information or control commands. Of course, cyber-physical systems may also be attacked by compromising their computing units.

Many cyber-physical systems operate under tight real-time constraints [START_REF] Lee | Cyber physical systems: Design challenges[END_REF]. This is especially the case with safety-critical systems such as automobiles and airplanes. In these systems, disrupting the timing can significantly impact system behavior, even when all the commands and sensor information are correct.

Manipulations performed on cyber-physical systems and the effects of the manipulations are not necessarily in the same domain [START_REF] Yampolskiy | Taxonomy for descriptions of cross-domain attacks on CPSs[END_REF][START_REF] Yampolskiy | A language for describing attacks on cyber-physical systems[END_REF]. While most cyber-physical system attacks focus on manipulations in the cyber domain that lead to effects in the physical domain, this does not have to be the case. Manipulations in the physical domain can cause effects in the physical and/or cyber domains.

Stuxnet is the most famous example of attacks on a production cyberphysical system [START_REF] Falliere | Stuxnet Dossier, Version 1.4[END_REF]. The attacks were performed by malware installed on programmable logic controllers that managed centrifuges at a uranium enrichment facility in Natanz, Iran. When activated, the malware caused the centrifuges to rotate at speeds much higher and much lower than the normal operational speed. The attacks reportedly damaged more than 1,000 centrifuges at the uranium enrichment facility.

Additive Manufacturing Security

Several researchers have analyzed 3D printers and 3D printing processes for vulnerabilities. Networking and communications systems have been found to lack integrity checks when receiving design files [START_REF] Turner | Bad parts: Are our manufacturing systems at risk of silent cyberattacks[END_REF]. Furthermore, communications protocols employed by desktop 3D printers can be exploited, enabling the retrieval of current and previously-printed 3D models, the termination of active printing jobs and the submission of unauthorized (new) jobs [START_REF] Do | A data exfiltration and remote exploitation attack on consumer 3D printers[END_REF]. Software and firmware commonly used in desktop 3D printers contain numerous vulnerabilities [START_REF] Moore | Vulnerability analysis of desktop 3D printer software[END_REF]. A phishing attack can be used to install a backdoor that enables arbitrary, targeted manipulations of design files by a remote adversary [START_REF] Belikovetsky | dr0wned -Cyber-physical attack with additive manufacturing[END_REF]. Malicious software installed on a computer can be used to automate manipulations of design files [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF]. Firmware installed on a 3D printer can be malicious [START_REF] Moore | Implications of malicious 3D printer firmware[END_REF] or compromised [START_REF] Xiao | Security attack on 3D printing[END_REF], enabling a range of manipulations of the manufacturing process. A range of physical attacks are also possible; manipulations of source materials can have far-reaching impacts on manufactured objects as well as on 3D printers and their manufacturing environments [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF].

One of the broadly discussed topics in additive manufacturing security is the impact on intellectual property. Numerous problems have been identified by researchers who have analyzed the legal aspects of intellectual property protection in additive manufacturing environments [START_REF] Brown | Legal aspects of protecting intellectual property in additive manufacturing[END_REF][START_REF] Holbrook | Digital patent infringement in an era of 3D printing[END_REF][START_REF] Tran | The law and 3D printing[END_REF] . For example, a 3D scan of a manufactured object is not considered to be an original technical drawing (blueprint) and, therefore, does not have the same legal protections [START_REF] Brown | Legal aspects of protecting intellectual property in additive manufacturing[END_REF]; thus, it can be used to circumvent copyright protection.

On the technical side, several attack schemes have been discussed and partially evaluated. In a scenario where additive manufacturing is outsourced, a malicious actor can assume the role of a manufacturing service provider and gain unrestricted access to design files and related specifications [START_REF] Yampolskiy | Intellectual property protection in additive layer manufacturing: Requirements for secure outsourcing[END_REF]. Sidechannel emanations can be recorded and analyzed in order to steal designs, even when a 3D printer is air-gapped. Analyses of the acoustic emanations of desktop 3D printers have enabled the reconstruction of the geometries of printed objects [START_REF] Faruque | Acoustic side-channel attacks on additive manufacturing systems[END_REF][START_REF] Song | My smartphone knows what you print: Exploring smartphone-based side-channel attacks against 3D printers[END_REF]. Researchers have also demonstrated that similar attacks are possible using infrared imaging [START_REF] Faruque | Forensics of Thermal Side-Channels in Additive Manufacturing Systems[END_REF] and magnetic side channel analysis [START_REF] Hojjati | Leave your phone at the door: Side channels that reveal factory floor secrets[END_REF].

It is important to note that, in the context of additive manufacturing, intellectual property is not limited to the specifications of the 3D object geometry -it can also include the specifications of the properties of the manufactured object and the manufacturing process parameters that ensure the fulfillment of the physical requirements [START_REF] Yampolskiy | Intellectual property protection in additive layer manufacturing: Requirements for secure outsourcing[END_REF]. Physical watermarking techniques have been discussed as a means to protect intellectual property in additive manufacturing environments [START_REF] Macq | Applicability of watermarking for intellectual property rights protection in a 3D printing scenario[END_REF].

Another broadly discussed threat category is the ability to inflict physical damage, especially when the quality of a manufactured part is sabotaged. Part quality can be degraded by introducing defects such as voids (internal cavities) [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF] or printing portions of objects with the wrong or contaminated materials [START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF]. The sizes of the defects and their geometries and locations define the extent of manufactured part degradation [START_REF] Belikovetsky | dr0wned -Cyber-physical attack with additive manufacturing[END_REF]. Other manipulations involve changing the orientation of a printed part [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF][START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF], introducing additional skew along one of the built axes [START_REF] Chhetri | KCAD: Kinetic cyber-attack detection method for cyber-physical additive manufacturing systems[END_REF], changing the thickness of layers [START_REF] Moore | Implications of malicious 3D printer firmware[END_REF] and varying manufacturing parameters such as the energy of the heat source and scanning strategy in the case of the powder bed fusion process [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF]. Manipulations of network command timing [START_REF] Pope | STPA for additive manufacturing[END_REF], energy supply [START_REF] Pope | STPA for additive manufacturing[END_REF] and source material composition [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF] have also been identified as potential means of sabotaging parts. Indeed, in the case of additive manufacturing of metal parts, manipulations of manufacturing parameters can damage the additive manufacturing machinery itself and even contaminate the manufacturing environment [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF][START_REF] Yampolskiy | Using 3D printers as weapons[END_REF]. Researchers have proposed game theory [START_REF] Xu | Cross-layer secure cyber-physical control system design for networked 3D printers[END_REF] and side-channel emanation monitoring [START_REF] Chhetri | KCAD: Kinetic cyber-attack detection method for cyber-physical additive manufacturing systems[END_REF] as approaches for combating sabotage in additive manufacturing environments.

Manufacturing Workflows

This section describes the additive and subtractive manufacturing workflows.

Additive Manufacturing Workflow

Figure 1 presents an additive manufacturing workflow. This workflow is increasingly common when additive manufacturing is offered as a service. As of January 2017, the 3D Printing Businesses Directory lists 892 companies as offering 3D printing services (3dprintingbusiness.directory).

Additive manufacturing equipment (which includes but is not limited to 3D printers) is usually developed and provided by an original equipment manufacturer (OEM). In 2015, 62 system manufacturers in 20 countries produced and sold industrial-grade additive manufacturing equipment and hundreds of small companies offered desktop 3D printers [START_REF] Associates | Wohlers Report[END_REF]. Firmware and software updates for additive manufacturing equipment and controller workstations that extend functionality and fix bugs are provided by third-party companies. Open-source software is also commonly used by desktop 3D printers. Various mechanical, electrical and electronic components (motors, filters, etc.) are required for replacement purposes; these are sold by original equipment manufacturers or third-party companies and shipped directly to customers.

3D object blueprints are provided in the stereolithography [START_REF] Hiller | STL 2.0: A proposal for a universal multi-material additive manufacturing file format[END_REF] (STL) format, additive manufacturing file (AMF) format [START_REF]American Society for Testing and Materials, ISO/ASTM52915-16: Standard Specification for Additive Manufacturing File Format (AMF)[END_REF][START_REF] Lipson | AMF tutorial: The basics (Part 1)[END_REF] or 3D manufacturing format (3MF) [START_REF]3MF Consortium, 3D Manufacturing Format, Core Specification and Reference Guide, Version 1[END_REF], each of which specifies the computer-aided design (CAD) model of the 3D object to be manufactured. Object blueprints specified in STL/AMF/3MF files are often provided by external 3D object designers directly to additive manufacturing service providers. Another scenario involves a design being provided by the end-product customer who created the design (common for enterprise customers) or purchased it from a designer (common for individual customers).

At the additive manufacturing service provider's facility, an STL/AMF/3MF file can be directly transferred to a 3D printer (via a computer network or USB stick) or interpreted by the controller workstation. In the latter case, the workstation sends the 3D printer individual control commands (often in Gcode [START_REF]1980: Interchangeable Variable Block Data Format for Positioning, Contouring and Contouring/Positioning Numerically Controlled Machines[END_REF], a language commonly used in computer-aided manufacturing) or as a tool path file containing a sequence of (often proprietary, 3D printer-specific) commands [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF].

Additive manufacturing requires electricity and a variety of source and auxiliary materials. While source materials are included in the end-product, auxiliary materials support or enable production in some way. For example, support material structures enable the printing of complex geometries and inert gas (e.g., argon) is often employed when a laser is the heat source. Source materials for plastic printers are usually supplied by original equipment manufacturers; the source material market for metal printers is more open [START_REF] Associates | Wohlers Report[END_REF].

Depending on the additive manufacturing process, source material and part geometry, the production workflow can include several post-processing steps (not shown in Figure 1). The removal of support structures is a common step in the case of plastic objects; metal parts require hot isostatic pressing, finish machining and surface finishing. For functional parts, non-destructive testing is usually the final step. The Wohlers Report [START_REF] Associates | Wohlers Report[END_REF] lists several nondestructive testing methods commonly used in traditional subtractive manufacturing that are inadequate to validate the quality of additively-manufactured parts; these include fluorescent penetrant inspection (FPI), radiographic inspection and computed tomography (CT). Also, the inability to detect small defects in additively-manufactured parts using an ultrasonic c-scan has been reported [START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF]. After all the required production and post-production steps are completed, the manufactured objects are delivered to customers via physical carriers. In order to reduce the environmental impact and manufacturing costs, some of the source materials that remain after an additive manufacturing process may be recycled. This is especially true for power bed fusion in which thin layers of powdered source material (usually metal or polymer) are distributed in a bed and fused by a heat source (laser or electron beam). The exposure of unused powder to high temperatures causes the properties of particles to change (in the case of plastic) and/or the particles to agglomerate into large clusters. Both these changes can have negative impacts on the final product quality. Therefore, the remaining powder is often sieved and mixed with "virgin" powder in proportions that minimize the negative impact on part quality.

Table 1 summarizes the main aspects of additive and subtractive manufacturing and compares their workflows.

Subtractive Manufacturing Workflow

The subtractive manufacturing workflow presented in Figure 2 is broadly similar to the additive manufacturing workflow, but some notable differences exist. While a subtractive manufacturing facility may provide manufacturing services to external customers, it is significantly less common than in the case of additive manufacturing. Instead, a subtractive manufacturing provider typically supplies complete products as a commodity and the part designers are usually located "in house."

As with additive manufacturing, software and firmware updates and manufacturing jobs are initiated from a workstation. However, unlike additive manufacturing, software and firmware updates are usually provided by origi- mands (in G-code) sent from a controller workstation to a computer numeric control machine define the movements of tools that remove the extraneous material to create an object.

The source material used in subtractive manufacturing also differs. In additive manufacturing, the source material is usually in wire or powder form; in the case of subtractive manufacturing, solid blocks of material are used. Auxiliary material is less important in subtractive manufacturing; for example, a stream of water can be used to remove shavings or cool a manufactured part. While a computer numeric control machine also uses electrical power, its importance to the process is different (this is discussed in more detail below).

Probably the most obvious difference between the additive and subtractive manufacturing workflows is that subtractive manufacturing has no material recycling. Instead, subtractive manufacturing requires assembly; this is because subtractive manufacturing is limited to defining the external shape of an object -if internal structure is needed, the object is produced by assembling multiple components, each of which is manufactured separately.

Finally, the two workflows have different numbers of actors and different actor relationships. Subtractive manufacturing usually has long-term, highvolume customer-provider relationships; in additive manufacturing, these relationships are short-term with small production runs. Additionally, the number of additive manufacturing service providers is growing rapidly. This is because the same additive manufacturing equipment can be used to produce a variety of 3D objects. In contrast, subtractive manufacturing often requires highly specialized equipment.

Attack Analysis Framework

This section presents a framework for analyzing attacks on or using computeraided manufacturing. The security threat categories for additive manufacturing are also presented. The threats are also relevant to subtractive manufacturing.

Attacks

Figure 3 presents key attacks on or using computer-aided manufacturing [START_REF] Yampolskiy | Using 3D printers as weapons[END_REF]. Several attack vectors can be used to compromise one or more elements of the manufacturing workflows shown in Figures 1 and2 (for additive manufacturing and subtractive manufacturing, respectively). For example, social engineering can be used to trick users into installing malicious software or firmware updates. The compromised element(s), their roles in a workflow and the degree to which an adversary can control the element(s) determine the specific manipulations that the adversary can perform. In conjunction with the manufacturing equipment, source materials and application area of the manufactured parts, the manipulations determine the achievable effects. In the case of a functional part of a device (e.g., jet engine blade), slight changes to the size or shape can render the entire device less efficient. Other changes may degrade the mechanical properties of a part so that it breaks during use [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF][START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF][START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF] or may cause material fatigue to develop much faster than expected [START_REF] Belikovetsky | dr0wned -Cyber-physical attack with additive manufacturing[END_REF].

Security Threat Categories

Only a fraction of the effects that can be produced by attacks intersects with the goals of an adversary. For example, not all changes to the internal geometry of an object (e.g., positions and sizes of internal cavities) would compromise the mechanical properties of the object. Furthermore, the goals and objectives (i.e., "stepping stones" for achieving the adversary's goals) differ across adversaries. For instance, a hostile nation state may be interested in compromising safety whereas a malicious competitor may be interested in increasing manufacturing costs. The intersection of the attack effects and adversarial goals are referred to as attack targets or threats because they are both achievable by an adversary and are of interest to the adversary.

Figure 4 presents the three major security threat categories (or attack targets) that have been identified for additive manufacturing. Two of the categories, theft of technical data and sabotage of additive manufacturing, are discussed in the research literature (see Section 2). A theft of technical data attack seeks to illegally replicate 3D objects or the manufacturing process itself. Sabotage attacks seek to inflict physical damage, e.g., by compromising the quality of manufactured parts or physically damaging additive manufacturing equipment.

Several articles discuss the misuse of 3D printers for manufacturing illegal items such as firearms and components of explosive devices [START_REF] Bilton | The rise of 3-D printed guns[END_REF][START_REF] Sternstein | Things can go kaboom when a defense contractor's 3-D printer gets hacked[END_REF][START_REF] Tirone | 3D printing: A new threat to gun control and security policy? The Conversation[END_REF]. With the exception of discussing the legal aspects [START_REF] Blackman | The 1st Amendment, 2nd Amendment and 3D printed guns[END_REF][START_REF] Johnson | Print, lock and load: 3-D printers, creation of guns and the potential threat to Fourth Amendment rights[END_REF][START_REF] Mcmullen | Worlds collide when 3D printers reach the public: Modeling a digital gun control law after the Digital Millennium Copyright Act[END_REF][START_REF] Tran | The law and 3D printing[END_REF], the research literature has largely ignored this last category.

Security Analysis

This section compares the additive and subtractive manufacturing paradigms from the security perspective. The analysis focuses on the similarities and differences in the manufacturing workflows discussed in Section 3. The analysis is structured according to the semantically-distinct elements of attacks on or using cyber manufacturing. The results are summarized in Tables 2 and3.

Attack Vectors

Additive and subtractive manufacturing equipment are cyber-physical systems. Both types of manufacturing have similar workflows with almost identical categories of actors and information, software and material flows. Cyber and physical attack vectors can be exploited to compromise various elements of the manufacturing systems. Therefore, the attack vectors that can be used to compromise the two types of systems are almost identical.

Many attack vectors considered in cyber security studies also apply to both workflows, enabling the compromise of cyber components. These include spear phishing, hacking, source file worms, etc. Because of the novelty and relative immaturity of additive manufacturing, additive manufacturing software and firmware are significantly more vulnerable to cyber attacks than their subtractive manufacturing counterparts.

Malicious insiders are a classical attack vector that can target both manufacturing workflows and compromise cyber and physical supply chains. Also, social engineering is applicable to both workflows.

However, certain differences between the attack vectors for the two workflows arise from the differences existing between the workflows themselves (Table 1). The following are the most notable differences that enable the attack vectors unique to additive manufacturing: Supplier-consumer relationships in additive manufacturing are significantly more dynamic and flexible than in subtractive manufacturing.

The number of potential suppliers in additive manufacturing is much larger than in subtractive manufacturing; this also includes third-party software and firmware providers.

The number of additive manufacturing service providers is much higher than in subtractive manufacturing. More importantly, they often provide manufacturing services instead of specific products. Object designers in additive manufacturing are primarily external actors while designers in subtractive manufacturing are internal actors (e.g., a division in an enterprise).

These differences induce attack vectors in additive manufacturing environments; the attack vectors are either new or have lower probability and limited impact in subtractive manufacturing environments. First, in an additive manufacturing environment, an adversary can assume one of two roles: (i) 3D object designer; or (ii) additive manufacturing service provider. These two roles are potentially malicious and do not require hacking, social engineering or some other means to compromise the actor. A malicious 3D object designer can create special files (e.g., with embedded worms) while a malicious additive manufacturing service provider could obtain access to and compromise STL/AMF/3MF 3D object blueprint files. Additionally, the involvement of third parties in software and firmware development provides opportunities for

Illegal Part Manufacturing

Access to illegal or illegally-manufactured items Required equipment is increasingly accessible -Blueprints are increasingly available on the Internet adversaries to develop and distribute malicious software and firmware, compromising equipment at additive manufacturing service provider sites.

Compromised Elements

According to Yampolskiy et al. [START_REF] Yampolskiy | Using 3D printers as weapons[END_REF], elements that can be compromised in the additive manufacturing workflow belong to four general categories: (i) actors or workflow roles assumed by the actors; (ii) software, firmware and/or hardware; (iii) network communications; and (iv) physical supply chain. An additional fifth category is power supply, whose impact on additive manufacturing is discussed in [START_REF] Pope | STPA for additive manufacturing[END_REF].

All five categories of elements can be compromised in additive and subtractive manufacturing environments. There are, however, noticeable differences at a fine level -based on where the elements belonging to the five categories are employed in a workflow and their purpose in the workflow. A major difference arises from the multiplicity and diversity of the auxiliary materials, and their significance on the quality of a manufactured part compared with traditional subtractive manufacturing. Another is that additive manufacturing employs several post-processing steps to improve part quality, some of which have limited or no importance in subtractive manufacturing (e.g., hot isostatic pressing). Furthermore, in some additive manufacturing processes, unused source material can be recycled and reused whereas in subtractive manufacturing, removed material is not directly reusable. Indeed, the recycling process in additive manufacturing itself can impact part quality because it affects the overall quality of the source material used. Additionally, the subtractive manufacturing workflow usually incorporates a step that is largely irrelevant in additive manufacturing -the assembly phase during which individually-manufactured pieces are assembled to create a functional part. Last, but not least, non-destructive testing equipment is commonly employed to verify the quality of manufactured functional parts; this equipment is largely computerized and, therefore, can be compromised by cyber attacks. Note that this is independent of reports that various non-destructive testing approaches used in subtractive manufacturing fail to detect defects in additively-manufactured parts [START_REF] Associates | Wohlers Report[END_REF][START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF].

Manipulations

Every compromised element of an additive or subtractive manufacturing workflow can be used as a staging point to compromise other workflow elements. The role of the compromised element(s) in a manufacturing workflow and degree to which an adversary exercises control over it/them determine the manipulations that are possible.

If the controller workstation or other computing equipment is compromised and connected to the Internet, classical cyber attacks that exfiltrate information or enable remote access via backdoors are possible. The attacks can be used to gain access to technical data, manipulate STL/AMF/3MF 3D object design files and modify key manufacturing process parameters. Illegal access to technical data or its use by an adversary who impersonates an additive manufacturing service provider are plausible attack vectors. An adversary who has direct access to equipment can also create restricted objects such as firearms; of course, this applies to both additive and subtractive manufacturing equipment that can produce the restricted objects or their components. Principal differences between additive and subtractive manufacturing technologies with regard to malicious manipulations are the broad availability of additive manufacturing equipment and access to object blueprints on the Internet.

A number of manipulations are possible if the controller workstation, additive manufacturing equipment or network communications between the controller and equipment are compromised. Since these three components comprise a cyber-physical system, most cyber-physical attacks are applicable to additive manufacturing systems. At the fundamental level, communications between a sensor and controller or between a controller and actuator can be interrupted or corrupted [START_REF] Cardenas | Secure control: Towards survivable cyber-physical systems[END_REF]. Furthermore, in the case of real-time processes -characterized by cyber-physical systems in general and additive/subtractive manufacturing systems in particular -the correctness of operations and their timing are of paramount importance [START_REF] Lee | Cyber physical systems: Design challenges[END_REF]. Therefore, manipulations of sensor readings and control commands involve disruptions of their timing and order [START_REF] Pope | STPA for additive manufacturing[END_REF]. The physical process that is controlled by the particular control loop ultimately determines the effects of any manipulations; this applies to additive and subtractive manufacturing alike. Furthermore, the timing of manipulations in the manufacturing cycle influences the effects and their extent [START_REF] Krotofil | Vulnerabilities of cyber-physical systems to stale data -Determining the optimal time to launch attacks[END_REF].

Additive and subtractive manufacturing equipment require power. Power spikes can affect both types of equipment in a similar manner (e.g., damage electric motors). However, power supply interruptions have different impacts on the manufactured objects. In the case of subtractive manufacturing, power interruptions only affect the speed of production. However, interruptions can have severe impacts on additively-manufactured part quality (this is discussed this in more detail below); therefore, these manipulations are categorized as additive-manufacturing-specific. Similarly, manipulations (e.g., control loop attacks) that affect the durations of particular operations impact additive and subtractive manufacturing differently -manufacturing speed for both technologies and part quality, in addition, for additive manufacturing.

Manipulations of source materials are possible in additive and subtractive manufacturing, but the available manipulations can be quite different. In both cases, the chemical compositions of source materials can be changed. In subtractive manufacturing, the quality of the source material blocks can be manipulated (e.g., they can have different microstructures). In the case of additive manufacturing, properties such as the form factor can be manipulated (e.g., size and shape of the powder particles or diameter of the wire). Additionally, the chemical compositions of the auxiliary materials used in additive manufacturing can be manipulated. In general, the attack vectors that enable source and auxiliary material manipulations in additive manufacturing are manifold and are much easier to exploit than in the case of subtractive manufacturing.

Effects

This section discusses the effects of attacks on additive and subtractive manufacturing. The discussion of effects is structured in terms of the three security threat categories (Figure 4).

Theft of Technical Data. Two scenarios in which intellectual property violations are possible in additive and subtractive manufacturing are: (i) compromise of the cyber infrastructure of a benign manufacturer (e.g., when a controller workstation is connected to the Internet); and (ii) manufacturer is a malicious actor. In both cases, the adversary is interested in gaining access to the victim's intellectual property and eventually commits an infringement.

The differences between the two manufacturing technologies arise from what is considered to be intellectual property. Clearly, 3D object geometry corresponds to intellectual property in both manufacturing paradigms. However, in the case of subtractive manufacturing, the material properties of the manufactured part depend directly on the properties of the source material block. In contract, in additive manufacturing, the part material is created (and, thus, its properties are defined) during the manufacturing process itself. Therefore, in additive manufacturing, the required properties of the manufactured 3D object as well as the manufacturing process specifications correspond to intellectual property [START_REF] Yampolskiy | Intellectual property protection in additive layer manufacturing: Requirements for secure outsourcing[END_REF].

Sabotage.

The following scenarios enable the quality of a manufactured part and/or manufacturing equipment to be sabotaged: Manipulation of the object specifications regardless of its representation, which could be a STL/AMF/3MF file, individual G-code commands, toolpath file, etc.

Compromise of the cyber infrastructure of the manufacturing process.

Compromise of the physical supply chain of source and auxiliary materials.

Manipulation of the power supply.

As discussed in [START_REF] Xiao | Security attack on 3D printing[END_REF] and demonstrated experimentally in [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF], manufactured parts can be sabotaged by modifying their exterior shapes and dimensions, thereby affecting their integration; this is clearly applicable to both manufacturing paradigms. In particular, this is achieved by modifying object specification files or compromising the cyber infrastructure of the manufacturing process. For example, compromised firmware could manipulate the thickness of the printed layers in additive manufacturing [START_REF] Moore | Implications of malicious 3D printer firmware[END_REF].

However, several sabotage attacks are specific to additive manufacturing and are not possible in subtractive manufacturing. Introducing voids in a manufactured object [START_REF] Sturm | Cyberphysical vulnerabilities in additive manufacturing systems[END_REF] or replacing portions of a manufactured object with a different material [START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF] can degrade the physical properties of the object; this can also change the weight and weight distribution of the object [START_REF] Yampolskiy | Using 3D printers as weapons[END_REF]. Furthermore, various additive manufacturing parameters can be manipulated to affect the microstructure of the manufactured object and, thus, its physical properties; the parameters include build direction, heat source energy and scanning strategy [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF]. The ability to sabotage the quality of a 3D-printed part by manipulating some of these parameters has been proven experimentally [START_REF] Moore | Implications of malicious 3D printer firmware[END_REF][START_REF] Xiao | Security attack on 3D printing[END_REF][START_REF] Zeltmann | Manufacturing and security challenges in 3D printing[END_REF].

As Stuxnet [START_REF] Falliere | Stuxnet Dossier, Version 1.4[END_REF] and the Aurora experiment [START_REF] Swearingen | What you need to know (and don't) about the Aurora vulnerability[END_REF] have demonstrated, a cyberphysical attack that forces equipment to operate outside its designated operational ranges can induce physical damage. Such cyber-physical attacks, which exploit the fact that cyber components control physical processes, are applicable to both manufacturing paradigms. An attack that damages a process chamber used in additive manufacturing could release hazardous materials to the environment. This is a manufacturing-process-specific case of sabotage. An example is the release of the metal powder used in selective laser melting, which is hazardous because of the fine particle size (0.1 to 5 μm) [START_REF]Inside Metal Additive Manufacturing, The Role of (Super) Powders in SLM (www.insidemetaladditivemanufacturing[END_REF].

Last, but not least, in the case of additive manufacturing with metals, damage to the process chamber can lead to an explosion or implosion with subsequent damage to the manufacturing equipment environment and a likely fire [START_REF] Yampolskiy | Security challenges of additive manufacturing with metals and alloys[END_REF][START_REF] Yampolskiy | Using 3D printers as weapons[END_REF]. For example, when the heat source is a laser, the production chamber is commonly filled with inert gas to prevent an exothermic reaction; increasing the oxygen pressure can cause the combustible fine metal powder to explode. A vacuum environment is maintained to minimize the deflection of electrons when an electron beam is used as the heat source. Therefore, a slow leak is a more likely outcome of process chamber damage, but an implosion caused by a specially-crafted attack cannot be ruled out [START_REF] Yampolskiy | Using 3D printers as weapons[END_REF]. While safety mechanisms are implemented to shut down the heat sources used in additive manufacturing during safety-critical events, these mechanisms can be disabled by malicious or compromised 3D printer firmware. Illegal Object Manufacturing. Two possible scenarios for this threat category are: (i) an adversary owns the blueprint of a potentially illegal object and the requisite manufacturing equipment; and (ii) an adversary has access to the blueprint and the manufacturing equipment.

In these scenarios, there are no technical differences in using additive or subtractive manufacturing to produce illegal items. The only practical differences arise from two factors. First, high quality additive manufacturing equipment is increasingly accessible to private owners, unlike industrial-grade subtractive manufacturing equipment that is predominantly owned by enterprises. Second, STL/AMF/3MF blueprint files for 3D printing are widely available on the Internet (e.g., from makers' forums); in contrast, blueprints used in subtractive manufacturing are generally well protected because of their intellectual property value to subtractive manufacturing enterprises.

Conclusions

The rapid proliferation of additive manufacturing has raised concerns about its security. In this context, it is important to understand the extent to which additive manufacturing differs from traditional subtractive manufacturing. This chapter has evaluated the additive and subtractive manufacturing workflows, and has presented a framework for analyzing attacks on or using computer-aided manufacturing systems along with the major threat categories that target additive manufacturing. In particular, the framework was applied to identify how the differences in the workflows translate to the security domain.

The analysis concludes that, while there are overlaps in the security of additive and subtractive manufacturing, significant differences exist. This is true for all the components in the analysis framework -attack vectors, compromised elements, manipulations and attack targets. Two of the three major threat categories for additive manufacturing -theft of technical data and sabotage -differ considerably from subtractive manufacturing. However, in the case of the third category -illegal object manufacturing -no notable differences exist between additive and subtractive manufacturing. The results of this investigation coupled with the increasing importance of additive manufacturing in all the critical infrastructure sectors emphasize the need to address the security aspects in a comprehensive and timely manner.

Figure 1 .

 1 Figure 1. Additive manufacturing workflow.

Figure 2 .

 2 Figure 2. Subtractive manufacturing workflow.

Figure 3 .

 3 Figure 3. Attacks on or using computer-aided manufacturing (based on [49]).

Figure 4 .

 4 Figure 4. Major threat categories.

Table 1 .

 1 Comparison of additive and subtractive manufacturing.

		Additive	Subtractive
		Manufacturing	Manufacturing
	Actors
	Software	OEMs and commercial software developers
	Provider	Open-source/community	-
	3D Object	Increasingly individuals	Predominantly enterprises
	Designer	External to enterprise	Internal to enterprise
	Customer	Increasingly short term	Long term
	Relationship	Low volume	High volume
	Actors/Roles	High	Low
	Materials
	Source	Often wire or powder	Solid blocks
	Auxiliary	Extensively used Influence product quality	Barely used/relevant -
	Manufacturing Process
	G-Code	Deposited/fused material	Material to be removed
	Command	Exterior/interior geometry	Exterior geometry
	Defines	Object physical properties	-
	Power Outage	Impacts manufacturing speed Impacts part quality	-
	Timing	Impacts manufacturing speed Impacts part quality	-
	Maturity Level
	Workflows	Mature and well-established (manual and CAM)
	Software and	New and immature	Very mature with few bugs
	Firmware	with many bugs
	Quality Control	SM tools/approaches condi-tionally applicable	Tools/approaches well-established and understood
		Immature tools/approaches	Mature tools/approaches
	Availability/Accessibility
	Equipment	Enterprises/employees Private individuals	-
	Blueprints	Restricted access (enterprise-guarded intellectual property) Third-party commercial -
		and non-commercial websites
	nal equipment manufacturers or commercial software developers; community
	involvement in open-source efforts are negligible. Furthermore, control com-

Table 2 .

 2 Comparison of additive and subtractive manufacturing security.

		Additive	Subtractive
		Manufacturing	Manufacturing
	Attack Vectors
		Hacking
	External	Source file worms
	Adversary	Physical attack on physical supply chain
	Compromises	Social engineering
	Benign	Et cetera
	Workflow	Specially-crafted	-
		blueprint files
		Malicious software	-
		and/or firmware
	Malicious Actors Assume Existing Roles	3D object designer Service provider Software developer Source/auxiliary	----
		materials provider
	Compromised Elements
	Source Material	Often wire or powder	Solid blocks
		Roles/actors
	General Categories	Software, firmware and hardware Network communications Physical supply chain
		Power supply
	Workflow-	Post-processing	Assembly line
	Specific	Material recycling	-

Table 3 .

 3 Comparison of additive and subtractive manufacturing security.

		Additive	Subtractive
		Manufacturing	Manufacturing
	Manipulations
		Compromise another workflow element
		Information exfiltration
		Control loop attacks
		• False sensor reading is provided
		• False control action is provided
	General Categories	• Sensor reading is not provided or is not followed • Control action is not provided or is not followed • Control action is provided too late or is out of sequence
		• Control action is stopped too soon or is applied too long
		• Sensor reading is provided too late or is out of sequence
		• Sensor reading is stopped too soon or is applied too long
		Power supply spikes
		Source material chemical composition
		Source material properties	Source block quality
	Workflow-Specific	Auxiliary materials Power supply interruption Power supply properties	---
		Operation duration/speed	-
	Effects/Attack Targets
	Theft of Technical Data	3D object geometry Required properties Manufacturing process	--
		specifications
		Integration ability
		Equipment damage
		Environmental contamination
	Sabotage	of degraded part Physical properties	-
		Weight	-
		Weight distribution	-
		Implosion/explosion	-
		Environmental damage	-