Giacomo Giorgi

Fabio Martinelli

Andrea Saracino

Mina Sheikhalishahi

Walking Through the Deep: Gait Analysis for User Authentication through Deep Learning

Keywords: Gait recognition, Behavioral analysis, Seamless continuos authentication, Deep learning

Seamless authentication is a desired feature which is becoming more and more relevant, due to the distribution of personal and wearable mobile devices. With seamless authentication, biometric features such as human gait, become a way to control authorized access on mobile devices, without actually requiring user interaction. However, this analysis is a challenging task, prone to errors, with the need to dynamic adapt to new conditions and requirements, brought by the dynamic change of biometric parameters. In this paper we present a novel deep-learning based framework for gait-based authentication. The paper presents an in depth study of the building and training of a Recurrent Convolutional Neural Network with a real dataset based on gait reading performed through five body sensors. We introduce methodologies to further increase the classification accuracy based on data augmentation and selective filtering. Finally we will present a complete experimental evaluation performed on more than 150 different identities.

Introduction

Wearable technology is advancing at a fast pace, with a large interest in industrial and research world. More and more additional computing capacity and sensors are incorporated into smartphones, tablets, (smart)watches, but also shoes, clothes, and other wearable items. These enhanced objects act as enablers of pervasive computing [START_REF] Wu | Smartshadow: models and methods for pervasive computing[END_REF], collecting data used to provide additional smart services to their users.Several of these smart devices come equipped with built-in accelerometers and gyroscopes, which can be exploited to register the body motion of users [START_REF] Ren | User verification leveraging gait recognition for smartphone enabled mobile healthcare systems[END_REF], [START_REF] Bao | Activity recognition from user-annotated acceleration data[END_REF], monitoring the unique movement pattern of a user might become an excellent instrument for seamless authentication [START_REF] Sprager | Inertial sensor-based gait recognition: a review[END_REF]. However, the majority of current solutions for sensor based authentication, are mainly based on active behavioral mechanisms, which require direct user interaction [START_REF] Buriro | Hold and sign: A novel behavioral biometrics for smartphone user authentication[END_REF], having thus limited advantages compared to classical authentication mechanisms, such as PIN, passwords, or finger pattern recognition. Considering that each individual person has a unique manner of walking, gait can be interpreted as a biometric trait and consequently, the aforementioned inertial sensors have great potential to play an important role in the field of biometry [START_REF] Sprager | Inertial sensor-based gait recognition: a review[END_REF]. If correctly exploited, the gait can be used as a method for seamless continuous authentication, able to authenticate users of wearable devices continuously during time, without requiring any active interaction. In this paper, we present a deep study on gait analysis for identity recognition based on inertial sensors and deep learning classification. The presented methodology exploits a public dataset [START_REF] Zhang | Accelerometer-based gait recognition by sparse representation of signature points with clusters[END_REF] collected on a set of 175 users through five body sensors, presenting the design and implementation of a recurrent convolutional neural network for deep learning-based classification. Through experimental evaluation, we show the effectiveness of the methodology in recognizing single user on which the recurrent convolutional network has been trained on and also the ability of the presented system to understand if the monitored gait belongs to an unknown person. The results show an accuracy close to 1, demonstrating the feasibility of the presented approach as a methodology for seamless continuous authentication, which can be exploited by mobile and wearable smart devices. The contributions of this paper are:

-The presentation of a deep-learning method based on Recurrent-Convolutional-Neural-Network for identity classification through gait analysis; -An analysis of the sensor orientation problem solved considering the magnitude of the 3 axis acceleration vectors; -It will be detailed the process of designing the network and consecutive training based on analysis of a variable number of body sensors, implemented through the Keras framework for deep learning; -The paper will introduce a methodology for data augmentation which aims at increasing the classification accuracy and prevent overfitting, by generating well instrumented artificial data; -An extended analysis of the gait recognition will be discussed by proposing a threshold-based method to filter out outliers and increase the overall accuracy. Moreover a study on sensor filtering to demonstrate the high recognition accuracy with less sensor will be presented; -A study of cross session classification to understand the capability of the network to learn the different walking patterns of a person is also introduced;

This paper extends the one presented in [START_REF] Giorgi | Try Walking in My Shoes, if You Can: Accurate Gait Recognition Through Deep Learning[END_REF] by presenting as new contributions (i) the introduction of a new deep learning network based on Recurrent Convolutional Neural Network, which improves the accuracy in recognizing identities, (ii) the application of a sensor data orientation invariance method (iii) the sensor filtering approach and related experiments, (iv) a study on cross session classification. The rest of the paper is organized as follows: Section 2 reports background notions on gait analysis and deep learning. Section 3 describes the used dataset and the data preprocessing steps. Section 4 reports the description of the design and implementation of the network and its training metodology. Section 5 reports the classification results for different scenarios and reducing the number of sensors consideres. Section 6 lists some related work. Finally Section 7 briefly concludes proposing some future directions.

Background

In this section we present some background notions exploited in this work.

Gait Analysis

Gait is the motion of human walking, whose movements can be faithfully reflected by the acceleration of the body sections [START_REF] Zhang | Accelerometer-based gait recognition by sparse representation of signature points with clusters[END_REF]. Human gait recognition has been recognized as a biometric technique to label, describe, and determine the identity of individuals based on their distinctive manners of walking [START_REF] Alotaibi | Improved gait recognition based on specialized deep convolutional neural network[END_REF]. Basically, due to the fact that walking is a daily activity, human gait can be measured, as a user identity recognition technique, in daily life without explicitly asking the users to walk. This fact distinguishes gait from other accelerometer measurable actions, like gestures, as well as other commonly used biometrics, such as fingerprints, signatures, and face photos, whose data collection usually interrupts the users from normal activities for explicit participation [START_REF] Zhang | Accelerometer-based gait recognition by sparse representation of signature points with clusters[END_REF]. Moreover, since portable or wearable accelerometers are able to monitor gait continuously during arbitrary time period, accelerometer-based gait recognition would be especially great tool in continuous identity verification [START_REF] Gafurov | User authentication based on foot motion[END_REF].

Deep Learning

A neural network is a class of machine learning algorithms, in which a collection of neurons are connected with a set of synapses. The collection is designed in three main parts: the input layer, the hidden layer, and the output layer. In the case that neural network has multiple hidden layers, it is called deep network. Hidden layers are generally helpful when neural network is designed to detect complicated patterns, from contextual, to non obvious, like image or signal recognition. Synapses take the input and multiply it by a weight, where it represents the strength of the input in determining the output [START_REF] Lecun | Deep learning[END_REF]. The output data will be a number in a range like 0 and 1. In forward propagation, a set of weights is applied to the input data and then an output is calculated. In back propagation, the margin of error of the output is measured and then the weights accordingly are adjusted to decrease the error. Neural networks repeat both forward and back propagation until the weights are calibrated to accurately predict an output [START_REF] Lecun | Deep learning[END_REF]. Network with many layers and many neurons that are fully connected can become computationally infeasible to train.

Convolutional Neural Networks Convolutional Neural Networks (CNN), is born with the task to reduce the number of parameters to train, limiting the number of connections of the neurons in the hidden layer to only some of the input neurons. This connections are called local receptve field of the convolutional layer and the weights of that region are shared. Each group is generally composed by as many neurons as needed to cover the entire input data. This way, it is as if each group of neurons in the hidden layer calculated a convolution of the input data with their weights. The results of the convolution is a feature. Commonly a pooling layer is applied to the result of the convolutional layer. It permits to provide translation invariance of the learned features and to reduce the dimensionality of the neurons. The result is a smaller version of the input features. These steps can be repeatedly applied as many times as desired: a new convolutional layer can be applied on the pooled layer, followed by another pooling layer, and so forth. The majors advantages of the CNN are the reduction of the network parameters thanks to sharing weights and the automatic features extraction at different semantic concept layer from the lower to the higher level representation, which provides a better data representation than the hand crafted feature descriptor. Recently, CNNs have been used as very powerful technique to solve and advanced the state-of-the-art accuracies in computer vision tasks such as face recognition [START_REF] Parkhi | Deep Face Recognition[END_REF], object recognition [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF].

Recurrent Neural Networks Recurrent Neural Networks (RNNs) are successfully applied to model sequential informations such as speech recognition [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF], language translation [START_REF] Cho | Learning phrase representations using RNN encoderdecoder for statistical machine translation[END_REF] and so on. Different from the traditional neural networks it assumes the values of the input sequence dependent between them. RNNs perform the same computation for every element of the input sequence and the output is dependent on the previous computation. Bidirectional RNNs is a variant of RNN based on the idea that the output at a certain time is dependent not only on the previous element but also on the future element of the sequence.

DATASET DESCRIPTION AND PROCESSING

In what follows, we present in detail the dataset description and the preprocessing steps done in order to prepare the data for the classification process.

Dataset Description

In this study, we exploit the ZJU-gaitAcc dataset that is public available and described in [START_REF] Zhang | Accelerometer-based gait recognition by sparse representation of signature points with clusters[END_REF]. This dataset contains the gait acceleration series of records collected from 175 subjects. Out of these 175 series, we consider the records related to 153 subjects, which are divided in two sessions, such that the first session represents the first time that data has been collected, while the second session shows the second time that the data has been recorded. For the remaining 22 identities only a single session has been recorded, hence they have been discarded for the classification task, but they have been considered as unknown subjects to estimate the ability of the network to understand if a monitored gait belongs to an unknown person. For each subject, the time interval between first and second data acquisition varies from one week to six months. For each subject, six records are presented in each session, where every record contains 5 gait acceleration series (Normally composed by 7-14 full step cycles) simultaneously measured with 5 sensor placed on the right wrist, left upper arm, right side of pelvis, left thigh, and right ankle as depicted in Fig. 1.

The acceleration readings have been measured at 100 Hz in straightly walks, through a level floor of 20m length. The raw data for each recording are composed by the x, y and z acceleration series during time.

Data Processing

The data processing part can be summarized in four main steps, namely cycles extraction, filtering, and normalization, respectively. In addition in this section is described a data augmentation process in order to generate more sintetic data to improve the accuracy and prevent overfitting.

Cycles Extraction. The gait cycle is used to simplify the representation of complex pattern of human walking. It starts with initial contact of the right heel and it continues until the right heel contacts the ground again. When the heel touches the ground, the association between the ground reaction force and inertial force make the z -axis signal strongly sensitive to change, forming peaks with high magnitude. Those peak points are utilized to identify the gait cycles. The ZJU dataset provides the manual annotations of the step cycles.

Cycles Normalization. Each gait cycle differs in terms of duration, due to the different speed which varies during walking, but not in shape. In ZJU dataset, the majority of cycles are constituted by a length between 90 and 118 samples. The features extraction phase performed by the CNN requires in input a fixed number of samples for each gait cycle. For this reason each gait cycle is normalized to a length of 118 samples through linear interpolation [START_REF] Coursey | Linear interpolation lattice[END_REF].

Noise reduction. The data collected from accelerometer sensors are affected by several noise sources due to the nature of the sensor. To reduce it and improving dataset quality, a filtering step is required. To this end a low pass butterworth filter [START_REF] Van Vollenhoven | Transient response of Butterworth filters[END_REF] is applied to smooth the signal and remove high peaks.

Magnitude computation. Most gait recognition studies that employ wearable sensors, consider as unreal assumption that the position and the orientation of the sensors do not change over time. Altough the ZJU dataset not suffers of this problem, in order to reproduce a more realistic result, we applied a sensor data transformation to remove the effect of sensor orientation from the raw sensor data. To this end, instead to consider, as input of the network, the 3-axis accelerometer vectors, we simply considered the magnitude of the acceleration vectors computed as the euclidean norm as: magnitude = x 2 + y 2 + z

DATA ANALYSIS

In this section we describe the design and implementation of the recurrent convolutional neural network, how it has been trained and the metrics used to evaluate the proposed method are given.

Network description and Training

In this paper, we proposed a deep neural network architecture applied to the problem of gait classification of 153 persons. Given a gait cycle, the task is to determine to which person the cycle belongs. We designed and implemented two network architectures suitables respectively for single sensor and multiple sensors experiments. As reported in Figure 2, both the single sensor and the multiple sensors architectures are based on the same core. It extracts, from a single input gait cycle magnitude, features of two different abstraction level and applies a temporal aggregation on the features extracted in the second level. The first two level features are extracted automatically from the input data through two stacked 1D convolutional layers, which compute respectively 128 and 256 features vectors with kernel size 2 and 3. The second features vectors level is passed to a bidirectional recurrent layer based on Gated Recurrent Units (GRU) [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF] with 256 neurons. It produces a temporal aggregation feature vector that is passed to two different pooling layers, which compute a feature subsampling respectively using the average and the maximum pooling. The functionality of that layer is to reduce the spatial size of the representation reducing the amount of parameter to train. The final result is the concatenation of the pooling results that represent the feature vector extracted from the input gait cycle. Each one of the convolutional layer output are passed through a batch normalization layer to regularize the model then trhough a Rectified Linear Unit (ReLu). In the single sensor scenario, the feature vector extracted are passed directly to a fully connected classifier containing 153 softmax units which compute the probability of the input gait cycle to belong to a specific subject. Thus, in the multiple sensor scenario, the feature vector is calculated for each sensor, aggregated with a concatenation operation and finally passed to the fully connected classifier. Our recognition problem is posed as a classification problem. Training data are groups of accelerometer data labeled with the owner identity. The optimization objective is average loss over all the identities in the data set. The loss is used in backpropagation steps, in order to update the weights. We used Adam optimization algorithm [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] to update network weights iterative based in training data. We start with a base learning rate of 0.001 and gradually decrease it as the training progresses. We use a momentum of µ = 0.9 and weight decay λ = 5 • 10 -4 . With more passes over the training data, the model improves until it converges.

Evaluation metrics

Gait recognition is the process of assign a given waling gait pattern to its own identity. In our case we consider as gait pattern a walking step cycle. As described in Section 4.1, the gait recognition network returns a probability vector, which reports the belonging probabilities of the given gait cycle for each subject class. Sorting the resulting probability vector, can be possible determine in which rank the given gait cycle has been assigned. Thanks to that it is possible, not only compute the recognition accuracy on the first rank (1-rank), but also the accuracy in recognizing the identity within the top k-ranks. More in detail the recognition accuracy of unseen walking record at 1-rank is given by the number of step cycles correctly identified at 1-rank, divided by the total number of step cycles in the walking record. In the same manner the accuracy at 2-rank is given by the number of step cycles correctly identified at 1-rank or 2-rank divided by the total number of step cycles in the walking record. In the same way is computed the accuracy for the rest of ranks. In order to evaluate the recognition capacity of the method presented, the following metrics are introduced (i) the overall recognition accuracy of unseen walking record on the 1-rank; (ii) the overall recognition accuracy of unseen walking record on the top k ranks, with 1 < k < 153. Finally another important statistics to consider are the mean value and the standard deviation of the correct and wrong probabilities predicted at 1-rank: P T 1rank , StDev T 1rank , P F 1rank , StDev F 1rank . They provide us an estimation of the difference between the resulting probabilities of the corrects and wrong predictions that can be useful to determine the probability threshold as discussed in Section 5.1.

EXPERIMENTAL ANALYSIS

In this section we report the description and results of the performed experiments to evaluate the effectiveness of the proposed methodology. It has been analyzed the accuracy of the gait recognition network on the ZJU dataset in cross and single session, first, considering all the sensors, then reducing the number of sensors considered. Finally we proposed metodologies to improve the overall accuracy based on data augmentation and filtering threshold.

Single session. In this scenario, we explored the capacity of the network in recognize a subject in one single session (walking gaits recorded in the same day). To this end, we consider only session 1 splitting the data as reported in Figure 3(a). For each subject we considered the first five walking records as training set and the sixth walking record as testing set. This setting is better suited for training because it uses 80% of dataset, 5.850, for training (about 38 gait cycle per identities), and roughly 20% for testing, 1.453 testing samples. After the augmentation, which is applied only on the training set, the number of training samples becomes 17.550 (about 114 gait cycle per identities). Figure 4(a), shows the CMC curve that reports the recognition accuracy for the single session scenario using 5 sensors. At 1-rank the accuracy is 99.06% with augmentation and 98.86% without augmentation. Furthermore an accuracy of 100% at rank 17 and 28 is achieved respectively for the augmentation and non-augmentation experiments.

Cross session. In this scenario, we explored the capacity of the network to learn different walking pattern of the same user. To this end we considered the two sessions recorded over time. As reported in Figure 3(b) we split the data considering the first five walking records of both sessions as training set and the last walking record of both sessions as testing set. The total amount of data is 11.748 for the training set before augmentation and 2.933 for the testing set.

Figure 4(b), shows the CMC curve that reports the recognition accuracy for the cross session scenario using 5 sensors. At 1-rank the accuracy is 98.70% with augmentation and 97.50% without augmentation. We obtain an accuracy of 100% at rank 20 and rank 35 respectively for the augmentation and non augmentation experiment.

Sensor Filtering

As an additional set of experiments, we have evaluated the accuracy results by considering different subsets of the five initial sensors. Conducting this type of experiments we evaluated the behavior of the proposed method in recognizing gait cycles in a less intrusive way. Considering the powerful of the data augmentation in improving accuracy as demonstrated in Figure 4, the results reported for the cross filtering refer only to data augmentation case.

Single session filtering experiments

As first experiment we explored the capacity of recognition exploiting a single sensor. The Figure 5 As aspected, increasing the number of sensors, the 1-rank accuracy increase considerably. The 1-rank accuracy in the real case scenario is 96.62% reaching 100% of accuracy at rank 21.

Cross session filtering experiments

The same experiments have been conducted in the cross session scenario. Figure 6(a), shows the CMC curves comparison between the five sensors taken standalone. The 1-rank accuracies are slightly lower than the single session scenario due to the fact that the network has to associate different gait patterns to the same identity. However, the most promising sensor remains the right side of pelvis with 87.51% of recognition accuracy at 1-rank. On the basis of single sensor accuracy, we tested the following sensors combinations: S3-S2 (right side of the pelvis-left upper arm), S3-S2-S5 (right side of the pelvis-left upper-right ankle) and S3-S2-S5-S4 (right side of the pelvis-left upper-right ankle-left thigh). The CMC curve is reported in Figure 6(b). The 1-rank accuracies are approximately 0.60% lower respect to the single sensor scenario. The 1-rank accuracy in the real case is 94.35% with 100% accuracy at rank 68. The differences between the single and cross session scenario is valuable only increasing the 1-ranks. In fact 100% accuracy is reached at very high ranks in the case of cross session respect to the single one. This is due to the fact that in the cross session the network assignes very low probabilities to the false negative gait cycles.

Threshold method based Another important statistics to considering are the mean value probability and its standard deviation of correct and wrong predictions (P T 1rank ,StDev T 1rank ,P F 1rank ,StDev F 1rank ). We computed this values for the sensors combinations reported in the Table 1. Since the probabilities for true positives is much higher than the one for false positives, it is possible to set a probability threshold to distinguish these two values. This leads to improve the overall recognition accuracy. In fact, setting a probability threshold equal to the mean probability of the correct prediction minus its mean standard deviation as: threshold = P T 1rank -StDev T 1rank and filtering out all values lower than the threshold, grants a recognition accuracy of 100%.

Unknown identities recognition

The proposed method, is only able to classify identities on which it has been trained on. Hence, if presented with a set of steps coming from an unknown identity, the Recurrent Convolutional Neural network will try to match the new gait with a known one. However, we argue that is still possible exploiting the RCNN to understand if a set of steps is belonging to an unknown identity rather than to a known one. It is worth noting that such a feature would be useful in the design of anti-theft applications for mobile and wearable devices. To this scope, we exploited the 22 unknown identites presented on the dataset to only one session and we measured the mean probability of the false positive prediction in cross and single session scenario. Table 2 shows that. It is evident as the predicted probabilities of the unknown identities is highly lower than the known

Related Work

In [START_REF] Choudhury | Robust view-invariant multiscale gait recognition[END_REF] a two-phase view-invariant multiscale gait recognition method (VI-MGR) is proposed, which is robust to variation in clothing and presence of a carried item. In phase 1, VI-MGR uses the entropy of the limb region of a gait energy image (GEI) to determine the matching gallery view of the probe using 2-dimensional principal component analysis and Euclidean distance classifier. In phase 2, the probe subject is compared with the matching view of the gallery subjects using multiscale shape analysis. In [START_REF] Zou | Robust gait recognition by integrating inertial and RGBD sensors[END_REF], the three types of sensors, color sensors, depth sensors and inertial sensors, are combined for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. Being based on deep learning, the accuracy of our framework is increased if the training is performed with a larger and diverse dataset. However, real data collection could be an issue which also brings privacy concerns. In [START_REF] Martinelli | Modeling privacy aware information sharing systems: A formal and general approach[END_REF] a framework for privacy preserving collaborative data analysis is presented, which could be exploited by our framework to increase the accuracy, without violating users' privacy. In [START_REF] Yang | iGAIT: an interactive accelerometer based gait analysis system[END_REF], an accelerometer-based gait recognition, named iGait, is proposed. The core function of iGAIT extracts 31 features from acceleration data, including 6 spatio-temporal features, 7 regularity and symmetry features, and 18 spectral features. The proposed framework has been used to analyze the gait pattern of 15 control subjects, where a (HTC) phone was attached to the back of participants by belts. In each trial, participants walked 25m along a hallway at their preferred walking speed. The first advantage of our approach comparing to what is proposed by Yang et. al [START_REF] Yang | iGAIT: an interactive accelerometer based gait analysis system[END_REF] is that deep-learning-based approaches learn features gradually. Hence, our methodology finds the most discriminating features through self training process. The second advantage is related to time needed to reach to 100% accuracy. In our approach 10 steps is enough to identify a person while in [START_REF] Yang | iGAIT: an interactive accelerometer based gait analysis system[END_REF] 25 minutes walk is required. At the end, the proposed approach in [START_REF] Yang | iGAIT: an interactive accelerometer based gait analysis system[END_REF] is evaluated through 15 subjects, whilst our technique is evaluated through 153 persons. The accelerometer-based gait recognition approach proposed in [START_REF] Zhang | Accelerometer-based gait recognition by sparse representation of signature points with clusters[END_REF] is evaluated on the same dataset we exploited in our experiments. The work, first consider the problem of step-cycle detection which suffer from failures and intercycle phase misalignment. To this end, an algorithm is proposed which makes use of a type of salient points, named signature points (SPs). Experimental results on the equivalent dataset of our experiment shows 1-rank accuracy of 95.8% for identification and the error rate of 2.2% for user verification. However, this accuracy is obtained on 14 steps, while in our proposed approach 100% is achieved in 10 steps.

Conclusion and Future Work

Gait analysis is an enabling technology for seamless user authentication, still it requires fast, accurate and flexible mechanism for an effective classification.

In this paper we have presented a classification methodology based on deep learning, to perform accurate user recognition through gait analysis. The reported accuracy on the considered dataset made of more than 150 identities, has proven to be extremely precise, especially when to the standard classification process, we apply sensor filtering, data augmentation and threshold based analysis. Furthermore, we have demonstrated that the present approach is effective in recognizing users in a plausible use case where only sensors representing smartphone and smartwatch have been used, i.e. the authentication process does not require the presence of additional sensors whose only task is to perform the identification, instead it is integrated in popular personal items. As future work, we plan to consider a real use case, where the framework is directly installed on personal devices and the training and classification are performed at runtime. In addition, in order to obtain a more general architecture for authentication, we plan to explore a siamese neural network architecture training it starting from the features extracted by the network presented.

Fig. 1 :

 1 Fig. 1: Body sensors

2 .

 2 Data augmentation In order to improve the performance of the deep learning network and to prevent overfitting, we have artificially increased the number of training examples by data augmentation. It is the application of one or more deformations applied to the labeled data without change the semantic meaning of the labels. In our case, the augmentation is produced varying each signal sample with translation drawn from a uniform distribution in the range [-0.2, 0.2]. The process produces a copy of the original gait cycle different in values but with an equal semantic of the walking cycle. Starting from, approximately 95 gait cycles per identity, with augmentation we reached 190 gait cycle per identity, passing from 14.573 training data to 29.146.

Fig. 2 :

 2 Fig. 2: Recurrent Convolutional gait recognition network

Fig. 3 :

 3 Fig. 3: Training and testing sets

Fig. 5 :

 5 Fig. 5: Single session results.

Fig. 6 :

 6 Fig. 6: Cross session results.

Table 1 :

 1 Mean probability and standard deviation of correct (TP) and wrong (FP) predictionScenario Sensors P T 1rank StDev T 1rank P F 1rank StDev F 1rank

	Single session	5 sensors 0.99 real case 0.98	0.03 0.06	0.59 0.65	0.19 0.20
	Cross session	5 sensors 0.99 real case 0.97	0.04 0.08	0.54 0.62	0.20 0.20

Table 2 :

 2 Mean prediction probability and std-dev for unknown identities Scenario Sensors PF 1rank StDevF 1rank

	Single session	5 sensors 0.75 real case 0.73	0.15 0.23
	Cross session	5 sensors 0.78 real case 0.76	0.13 0.12

Table 3 :

 3 False positive and False negative varying threshold

	Scenario Sensors Threshold FP FN
	Single session	5 sensors real case	0.98 0.96	0 1.02 0.07 2.25
	Cross session	5 sensors real case	0.98 0.96	0.12 1.54 0.91 2.13

Acknowledgements

This work has been partially funded by EU Funded projects H2020 C3ISP, GA #700294, H2020 NeCS, GA #675320 and EIT Digital on Trusted Cloud IoT.


Blessings Pardon
email: blessings83@gmail.com

Stephen Maoneke

Naomi Flowerday
email: sflowerday@ufh.ac.za

Isabirye
email: nisabirye@ufh.ac.za

The Influence of Native Language on Password Composition and Security: A Socioculture Theoretical View

Keywords: passwords, password characteristics, socioculture theory, native language, security

This study investigates the influence of native languages on password composition and security. The socioculture theory's psychological development principles were used to argue the influence of language on passwords. 107 Namibian and South African university students were asked to generate a new password for the study using a web based experiment. Levenshtein's edit distance, language experts and a password guessing algorithm were used for data analysis. Results showed that users generated passwords that were oriented towards both English and native languages. English is the first language of instruction while native languages are typically the first spoken languages of the participants. These passwords were based on names and words. A difference in character distribution confirmed the disparity in character preferences among researched groups. These findings suggest the influence of psychological development according to the socioculture theory. Password guessing shows that English oriented passwords are weaker than those oriented in native languages. The study shows that choices of password generation policy design should be informed by contextual factors if they are to be effective.

Introduction

The idea of using passwords for authentication purposes can be traced to ancient Roman times [START_REF] Adeka | Resolving the Password Security Purgatory in the Contexts of Technology, Security and Human Factors[END_REF]. Today, passwords remain a dominant authentication mechanism, irrespective of research publications dating back to at least 1979 documenting various password security and usability limitations [START_REF] Bonneau | The Science of Guessing: Analyzing an Anonymized Corpus of 70 Million Passwords[END_REF][START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Wheeler | zxcvbn: Low-Budget Password Strength Estimation[END_REF]. As research is progressing, focus is on passwords of Western computer users (mainly English speaking) and the recent insurgence of studies on Chinese passwords [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. Little is known about passwords generated by African computer users. This coincides with growing evidence suggesting passwords can differ according to contexts. For instance, the success of probability guessing algorithms shows a reliance on the use of related password dictionaries, something that points to the influence of a user's language. In addition, the effectiveness of targeted attacks over trawling password guessing attacks further emphasizes the role of contextual factors in influencing password composition [START_REF] Wang | Targeted Online Password Guessing: An Underestimated Threat[END_REF]. The language orientation used during password generation is one of the factors promoting password composition disparities across different contexts [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF]. Even though most African languages are Latin character-based unlike Chinese languages [START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF], the socioculture theory argues that learning and thinking is not only a result of biological factors but contextual factors too. Hence, according to the socioculture theory, African passwords are expected to portray unique traits that reflect contextual factors.

In terms of language, Africa portrays a unique context where English is the dominant language of instruction and first written language in literacy, while native languages are the spoken languages [START_REF] Deumert | Texting Africa: Writing as Performance[END_REF][START_REF] Lexander | Texting and African Language Literacy[END_REF]. It is against this background that this study adopts a socioculture theoretical view in its investigation of the influence of native African languages on password composition and strength. There are suggestions that the use of multiple languages including native languages, promotes different password composition orientations, which have the potential to enhance password strength [START_REF] Voyiatzis | An Empirical Study on the Web Password Strength in Greece[END_REF]. From a business perspective, the African continent is experiencing growth in technology use such as social media, electronic mails, mobile phones, personal computers [START_REF] Stork | Internet Going Mobile: Internet Access and Use in 11 African countries[END_REF], internet banking, electronic commerce and adoption of cryptocurrenciesdevelopments that emphasize the importance of studying authentication mechanisms within the African context. Understanding the influence of native languages on password composition and strength will help understand users' misconceptions about password generation strategies from which viable solutions can be proposed. For instance, given a multilingual environment, African system administrators have difficulties in deciding which password dictionaries to consider when implementing the recently suggested password policy best practice of using a blacklist [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF].

This paper is organized as follows: the next section presents the theoretical foundation of this study. The section discusses characteristics of passwords guided by principles in the socioculture theory. This is followed by a presentation of the research methodology used in this study. A presentation of research findings and a discussion of these findings concludes the paper.

Theoretical Foundation

Information Systems is a multidisciplinary subject domain and where its theories cannot address certain problems, one can adapt theories from other research disciplines [START_REF] Shin | A Socio-technical Framework for Internet-of-Things Design: A Human-centered Design for the Internet of Things[END_REF]. This study adapts the socioculture theory. Lev Semyonovich Vygotsky is credited for laying out the theoretical framework of the socioculture theory [START_REF] Mercer | Explaining the Dialogic Processes of Teaching and Learning: The Value and Potential of Sociocultural Theory[END_REF]. The socioculture theory argues that an individual's mental function is related to participations in contextual social interactions. The socioculture theory drew from the Marxist theory and proposed three principles that explain psychological development: the genetic law of development, mediation and genetic domains. These principles have been widely used to explain high mental activities that are argued to be socially constructed instead of being biologically constructed alone [START_REF] Mercer | Explaining the Dialogic Processes of Teaching and Learning: The Value and Potential of Sociocultural Theory[END_REF][START_REF] Lantolf | Social Theory and Second Language Development[END_REF]. Accordingly, this study assumes that password generation and use are higher order mental activities involving voluntary attention, logical thinking and problem solving. The next section discusses principles in the socioculture theory, reflecting on their implications to password composition.

The Generic Law of Development

The generic law of development suggests that the settings of an individual as determined by culture, language, history, peer groups and institutional structures at school or workplace play a critical role in shaping the initial human mental development [START_REF] Lantolf | Social Theory and Second Language Development[END_REF].

It is argued that "human psychological process does not pre-exist inside the head waiting to emerge at just the right maturational moment" [17, p. 14]. Neither is mental development considered an inborn capacity that would naturally unfold with time. Instead, human psychological development occurs across two levels: first at a social level as one is interacting with those in one's social environment and then at an individual level. This suggests that the social environment in which a computer user resides/grew up has an influence on the password a user is likely to generate. In particular reference to Africa, the present language landscape portrays a multilingual society where individuals speak and write at least two different languages, which are expected to affect password composition. For example, South Africa (SA), Nigeria, Namibia, Ghana and Ethiopia, just to name a few, have more than ten recognised indigenous languages [START_REF] Deumert | Texting Africa: Writing as Performance[END_REF][START_REF] Lexander | Texting and African Language Literacy[END_REF]. Other contextual factors found influencing the composition of passwords include website information and website functionality [START_REF] Ur | I Added '!' at the End to Make It Secure": Observing Password Creation in the Lab[END_REF]. For instance, users use website information or website functionality as they adapt phrases such as "mail account", "rockyou", "news" or "amazon" in their passwords. Further to that, elements in the context of a user, such as individual names and those of geographical locations, are some of the strings that could be used when generating passwords [START_REF] Ur | I Added '!' at the End to Make It Secure": Observing Password Creation in the Lab[END_REF]. The next section on mediation reflects on possible effects of these contextual factors on password composition.

Mediation

The socioculture theory suggests different cultural artefacts (symbolic tools), such as language, are used to mediate social interactions and regulate cognitive activities of thinking and problem solving. These cultural artefacts differ according to contextual factors depending on the social environment, as explained by the generic law of development. This study focuses on user-generated passwords with the aim of determining how social contextual factors influence password composition. Due to the paucity of a publicly available password corpus of African computer users, this section makes reference to research contexts that have publicly available password corpora. The said contexts are able to demonstrate the influence of the generic law of development as reflected by a preferential use of mediating symbolic tools. For example, an analysis of more than 100 million publicly leaked English and Chinese passwords shows that close to 50% of Chinese passwords are purely digit-based when compared to English passwords that are mainly a concatenation of English words and digits or words in the English dictionary (25.88%) [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. In addition, one in every ninth Chinese user inserts a Pinyin name while one in four English users include their name when generating passwords. A closer analysis of these different password corpora shows that the respective passwords reflect the influence of native languages found across the analysed data sets [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF]. Affirming the use and influence of native language on password composition, Wang et al. [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF] observed a difference in character distribution between English and Chinese passwords. Further to that, Chinese computer users are more likely to use keyboard patterns as passwords compared to English computer users [START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF]. An analysis of the Chinese social context shows that few websites support characters of Chinese languages, something that is compounded by a society that is not well versed in the English language [START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. As such, the Chinese resort to keyboard patterns and the use of digits when generating passwords [START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. In addition, other isolated studies conducted in different countries confirmed the influence of native language (mediating tools) on password composition and structure. For instance, [START_REF] Voyiatzis | An Empirical Study on the Web Password Strength in Greece[END_REF] noted a small (2.5%) percentage of native Greek language-oriented passwords generated by Greek computer users. In another study, [START_REF] Bonneau | Of Contrase˜nas, ,!תואמסיס and 密密密码码码 Character Encoding Issues for wWeb Passwords[END_REF] noted passwords that were oriented towards native languages, namely, Hebrew and Spanish. Besides native language use, culture was found playing a critical role in influencing the composition of Chinese passwords. For instance, the numbers 6 and 8 are culturally believed to be lucky numbers in Chinese, hence these are regularly used [START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. Four, on the other hand, is an unlucky number and less frequently used [START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. Furthermore, the use of digits also portrays the pronunciation of certain specific phrases in Mandarin Chinese. For example, 5201314, a common password among Chinese users, translates to "I love you forever" [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF]. All these findings point to the influence of contextual factors in shaping password composition. Consequently, mediating tools (native and non-native languages) that shape psychological development in Africa are expected to influence the composition of user-generated passwords.

Generic Domains

The socioculture theory motivates the notion that higher order mental functionality is always in motion and goes through continuous changes [START_REF] Marginson | Vygotsky's Sociocultural Theory in The Context of Globalization[END_REF]. A generation inherits cultural artefacts from previous generations and acts on them; these modified artefacts are then passed on to the next generation. Such evolutions can be initiated by changes that occur in a society thereby enabling "changes in human consciousness and behaviour" [19, p. 119]. For instance, a longitudinal study by [START_REF] Von Zezschwitz | Survival of the Shortest: A Retrospective Analysis of Influencing Factors on Password Composition[END_REF] found that, user passwords evolved over time due to changes in password security requirements. Such evolutions involved minor changes to existing passwords as users adapted old passwords in an attempt to comply with password requirements, without compromising memorability. In addition, [START_REF] Jakobsson | The Benefits of Understanding Passwords[END_REF] observed that users can evolve their passwords by making spelling mistakes, insertions, concatenating different character classes and replacing different character classes, for example, LEET could be written as L33T [START_REF] Jakobsson | The Benefits of Understanding Passwords[END_REF]. Concatenation is the dominant password creation strategy that is mainly characterised by the LSD or LDS password structure where L represents alphabetic letters, S represents symbols and D is for digits.

Methodology

This study is aligned to the design science research methodology as it focuses on generating new knowledge by building (designing) and evaluating artefacts [START_REF] Baskerville | Genres of Inquiry in Design-Science Research: Justification and Evaluation of Knowledge Production[END_REF]. For evaluation, this study uses an experiment to gather data [START_REF] Baskerville | Genres of Inquiry in Design-Science Research: Justification and Evaluation of Knowledge Production[END_REF]. University students based in SA and Namibia participated in the experiment for this study. The literature showed that experiments and existing leaked password corpora are the commonly used data sources of passwords [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF][START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF]. There were also isolated reports on using interviews and questionnaires or a combination of both as password gathering techniques [START_REF] Von Zezschwitz | Survival of the Shortest: A Retrospective Analysis of Influencing Factors on Password Composition[END_REF]. However, one of the challenges associated with using leaked passwords is that details of password rules used in password generation often remain unknown. The influence of password meters and password policies is well documented in the literature and failure to articulate such details when reporting characteristics of leaked passwords makes contextualising research findings complex [START_REF] Von Zezschwitz | Survival of the Shortest: A Retrospective Analysis of Influencing Factors on Password Composition[END_REF][START_REF] Shay | Encountering Stronger Password Requirements: User Attitudes and Behavior[END_REF]. Further than that, some of the existing passwords may have been generated by password managers [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF]. Besides, findings from leaked passwords are biased towards successfully guessed passwords with characteristics of those unguessed passwords remaining unknown behind encryption and salting algorithms.

Conversely, [START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF] argues against the use of experiments, interviews and questionnaires as techniques for gathering user passwords and instead used existing leaked passwords. Yang et al. [START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF] is of the view that participants may not always reflect real-life password experiences; samples are often small, or the target group comprises students/company employees who could compromise the generalisability of findings. However, it is important to realise that experiments can be designed in such a way that participants can simulate password generation and treat the process in the same manner they would when generating real passwords [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF]. von Zezschwitz et al. [START_REF] Von Zezschwitz | Survival of the Shortest: A Retrospective Analysis of Influencing Factors on Password Composition[END_REF] justify the proxy of university students as they have found that, on average, computer users generate their first password at the age of 15 and these passwords often remain unchanged or experience minimal changes as users adapt their first passwords for different accounts.

The Experimental Design and Administration

This study adapted and modified an experimental framework used by [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF] and [START_REF] Komanduri | Modeling the Adversary to Evaluate Password Strength With Limited Samples[END_REF], a leading research group on password policies and guessing algorithms. Their experimental framework has been used widely to underpin research since 2011. This experimental framework gives user password generation conditions and keeps data for each user organised. The experiment of this study was based on a web application built specifically for the purpose of this experiment. Users (students) of the web application were asked to generate a password following specified password rules. Upon opening the password generation platform, participants were presented with a scenario encouraging the generation of a realistic password as purported by [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF] and [START_REF] Komanduri | Modeling the Adversary to Evaluate Password Strength With Limited Samples[END_REF]. This study adapted an existing scenario from [START_REF] Komanduri | Modeling the Adversary to Evaluate Password Strength With Limited Samples[END_REF] that was modified as follows: "Imagine that your main email service provider has been attacked and your account has been compromised. You need to create a new password for your email account, since your old password may be known to the attackers.

Because of the attack, your email service provider is also changing the provider's password rules. You are to generate a new password following new conditions."

Users were required to generate passwords following the comprehensive eight character (Comp8) password policy. The Comp8 password policy is a popular policy that was designed following a guideline by the National Institute of Standards and Technology (NIST) [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF]. Participants were required to generate a password that is at least 8 characters long, containing at least one: upper-case letter, lowercase letter, digit and special character. Furthermore, participants were asked not to use their name or personal details in their passwords, though no control was put in place to avoid participants from using personal information. After generating passwords, participants were asked to complete an online survey. The online survey gathered data on demographics and attributes for evaluating principles in the socioculture theory.

Data Analysis

Socioculture Theory Principles. This study used mediating symbolic tools to evaluate the generic law of development. It is argued that contextual factors influence psychological development as suggested by the socioculture theory. Hence, computer users are expected to reflect contextual factors by orienting user-generated passwords towards languages and following cultural practices that are common within their contexts. Language orientation in user-generated passwords was used to establish the influence of contextual factors. According to [START_REF] Lantolf | Social Theory and Second Language Development[END_REF], "language in all its forms is the most pervasive and powerful cultural artefact that humans possess to mediate their connection to the world, to each other, and to themselves" (p.5). Content analysis was used to identify the use of English and native languages in user-generated passwords. English is the official language in Namibia and SA. Levenshtein's edit distance was used to measure the distance between passwords and dictionary words [START_REF] Wang | Targeted Online Password Guessing: An Underestimated Threat[END_REF][START_REF] Campbell | Impact of Restrictive Composition Policy on User Password Choices[END_REF]. The edit distance shows the number of characters that need to be changed in order to convert a password to the closest dictionary word. Two language experts were engaged to identify passwords oriented towards native languages. The engaged experts consisted of a Namibian and a South African national. In addition, a frequency distribution of characters was used to establish the influence of contextual factors. Differences in character distribution between password corpora could be used as a basis to justify the differences between social contexts [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF].

Measuring Password Strength. This study considers password strength or security as a factor of the number of guessing attempts needed to guess a particular password by any given password guessing algorithm. Accordingly, considerations of password threats for this study are limited to online and offline threats where the perpetrator has an opportunity to make several password guessing attempts. This study used Dropbox's zxcvbn; an open source password guessing algorithm. The algorithm was introduced in 2012 and has seen various modifications to enhance guessing performance [START_REF] Wheeler | zxcvbn: Low-Budget Password Strength Estimation[END_REF]. Unlike Probabilistic Context-Free Grammar (PCFG) and Markov chain that use probability, zxcvbn uses heuristics to guess passwords. Hence, zxcvbn is a low cost password guessing algorithm that can work with a small password sample and does not require a powerful computer, as is the case with resource intensive algorithms such as the PCFG and Markov chain. Furthermore, zxcvbn shows to be a better password guessing algorithm than the currently commercialised measures and algorithms for guiding users to generate secure passwords [START_REF] De Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF]. When compared to leading password guessing algorithms such as the PCFG, zxcvbn is comparable to PCFG until 10^5 [START_REF] Wheeler | zxcvbn: Low-Budget Password Strength Estimation[END_REF]. Wheeler [4] gave a detailed overview of the version of zxcvbn considered in this study. It should be noted this study retained password dictionaries for zxcvbn that were used by [START_REF] Wheeler | zxcvbn: Low-Budget Password Strength Estimation[END_REF] and [START_REF] De Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF] to test the effect of language on password strength.

Findings and Results

This section presents findings and results from data collection and analysis.

Demographics

A total of 107 participants took part in the password generation experiment for this study. Demographic data on gender and age group was gathered. A total of 44% of the participants were female with 56% male students. Notably, 88% of the participants were 18 to 25 years old, and the remaining 12% were more than 25 years of age. The age groups of university students are consistent with those found in the literature [START_REF] Shay | Encountering Stronger Password Requirements: User Attitudes and Behavior[END_REF]. Moreover, 64% of the participants were Namibians with 28% representing South Africans. The remaining 8% represented other African countries.

Social Context Overview

Data was gathered to establish characteristics of the social context. This data was used to evaluate attributes of the generic law of development as purported in the socioculture theory. To evaluate the generic law of development, this study used data on user computer skills, first language, second language and ethnic group. In terms of computer use, the majority of participants indicated that they had had exposure to computers and had the know-how to use computers. The majority (98%) of the participants indicated that they had at least basic computer skills. Only 17% indicated that they were experts at using computers, while 47% indicated that they were above average. In addition, data gathered on first and second language showed that the most common first language was Oshiwambo (36%) from the Vambo tribe of Namibia, followed by isiXhosa (24%) from the Xhosa tribe of South Africa. The Vambos are the dominant tribe in Namibia while the amaXhosa are a dominant tribe of a targeted province of SA. There were fourteen other language varieties from different Namibian ethnic groups representing 1% to 5% of the participants. At least twelve ethnic groups were observed among Namibian participants. In particular to the targeted South African province, five diverse ethnic groups using different native languages were observed. Native languages from participants who came from countries other than Namibia and SA represented 7%. However, when all the participants were combined, 92% of the participants indicated that English was their second language. These findings suggest that participants in this study grew up speaking different native languages within their ethnic groups and went on to use English as a medium of instruction at learning institutions [START_REF] Deumert | Texting Africa: Writing as Performance[END_REF][START_REF] Lexander | Texting and African Language Literacy[END_REF]. This study posits that these unique social contexts played a critical role in participants' psychological development at both social and individual level. The next sections investigate how these contextual factors influence password composition.

Mediating Symbolic Tools and Password Composition

English-oriented Passwords. A review of user-generated passwords showed that English (47%) words or names were most commonly used to generate passwords. This could be explained by the fact that English is the first language of instruction and remains a dominant language of technologies used within the context of this study [START_REF] Deumert | Texting Africa: Writing as Performance[END_REF]. The edit distance for English-oriented passwords ranged from two to nine characters. The majority (26%) of passwords had an edit distance of three. Passwords with an edit distance of at least three characters are difficult to guess using a dictionary attack especially if a perpetrator does not have any prior knowledge of the password [START_REF] Campbell | Impact of Restrictive Composition Policy on User Password Choices[END_REF]. Nevertheless, a closer inspection of passwords oriented towards the English language showed that computer users are more likely to use an English word (57%) or English first name (43%) to generate a password. The majority (45%) of participants concatenated a name/word, number(s) and a symbol. Other participants (42%) combined a name/word, symbol and put numbers at the end when generating passwords. Another observation was the presence of passwords based on common English words such as P@55w0rd777, Ilove!995 and the use of a commonly used profane English word. Other common English words used when generating passwords included Smooth, Favour, Internet and Help. Arguably, these passwords reflect what [START_REF] Bonneau | The Science of Guessing: Analyzing an Anonymized Corpus of 70 Million Passwords[END_REF] referred to as global passwords.

Native Language-oriented Passwords. Content analysis showed that, of all the passwords gathered, 30% were oriented towards native languages; 9% were oriented towards multilingualism; 11% were considered random passwords, while 2% were based on keyboard patterns. An analysis of native language-oriented passwords showed that 76% of these were based on the name of a person. Most of these were Oshiwambo and isiXhosa names. Twelve percent (12%) of the passwords oriented towards native language were names of towns and districts in participants' contexts. The majority (38%) of passwords had an edit distance of three characters. Native language-oriented passwords had an edit distance ranging from two to sixteen characters. Users often concatenated a name with numbers and a symbol. The content analysis showed that the majority of multilingual passwords had traces of English and native languages.

Frequency Distribution of Characters. To establish the magnitude of influence by native languages on password composition, the distribution of characters in passwords of the researched contextual environments was extracted. Results showed that Namibian participants preferred characters "anoetumisrhlbdkcpygfwxjvqz" in descending order. South Africans' passwords assumed the "aneosmlituhyrbgpdwzvkqf" character distribution in descending order. For both samples, "a", "n", "o", "e" were popular characters among South African and Namibian passwords. However, there is a difference in the overall distribution of character use. The study posits that this finding points to differences in the social contextual environments that informed preferred mediating artefacts.

Passwords generated by South Africans and Namibians were combined to establish a joint password distribution in descending order: "anoetmiushlrbygdpkcfwvzxjq". Given that both SA and Namibia use English as their official language and as the first language of instruction in schools, it is interesting to establish how South African and Namibian passwords' character distribution compares to that of English users. This study uses a password distribution for English users that was generated from more than 33 million leaked English passwords [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF]. An analysis of these English passwords by [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF] established a character sequence "aeionrlstmcdyhubkgpjvfwzxq" in descending order. It is not clear to what extent password rules used in this study and that of existing passwords from English users influenced the preference of characters. When compared to the distribution of combined South African and Namibian passwords, it can be noted that characters "a", "o" and "n" are the most popular across the samples. However, as observed in the literature, there is a degree of difference in the order of preferred characters, which is attributed to a difference in languages used when orientating user generated passwords [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF]. These findings suggested that contextual factors did have an influence on password composition due to differences inspired by psychological development as suggested in the socioculture theory.

Password Generation Strategy

Participants were asked to indicate the strategy used to generate their passwords. The five most commonly used password creation strategies include adapting a name, adapting an existing password, using words in local language, using non-standard spellings and using a non-English language as shown in Figure 1. These findings were supported by findings from content analysis. Based on the research findings, it can be argued that the practice of adapting existing passwords promotes password evolution as suggested in the generic domain. In addition, it was observed that the selection of numbers in passwords was linked to a year or the age of participants. For instance, it was noted that passwords often had numbers within the range of 18 to 25 which might reflect the age of a participant at the time of generating the password. Furthermore, the majority of passwords had a number like 95 or 1995 which could be traced to the year of birth given the average age group of participants reported in section 4.1. In addition, other participants included digits such as 2017a number that reflected the current year at the time of password generation. These practices were also reported as common in the literature [START_REF] Wang | Understanding Passwords of Chinese Users: Characteristics, Security and Implications[END_REF][START_REF] Li | A Large-Scale Empirical Analysis of Chinese Web Passwords[END_REF][START_REF] Yang | An Analysis View on Password Patterns of Chinese Internet Users[END_REF].

Password Strength

This section reports on password strength as ascertained by the zxcvbn password guessing algorithm. Figure 2 summarises password strength according to five categories, namely very weak, weak, normal, strong and very strong, as proposed in the zxcvbn password guessing algorithm. Data analysis showed that more than 40% of the passwords were considered strong to very strong, according to password guessing results from zxcvbn. Results from zxcvbn showed that it would take at least three months to centuries to guess passwords classed as strong to very strong. This suggests an attacker would need many attempts to guess passwords within this category. Assuming a password attacker adopts an optimal approach to password guessing, passwords classified as very weak to normal are likely to be targeted first [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF]. Hence, 46% of user-generated passwords in this study are considered secure according to findings from zxcvbn. However, a closer look at the passwords showed that language orientation during password generation had had an effect on password strength. Passwords oriented in native languages were stronger than English passwords. Figure 3 shows the difference in password strength according to language orientation.

Very Weak, 13%

Weak, 20%

Normal, 21%

Strong, 22% Very Strong, 24% Fig. 3. The effect of language orientation on password strength.

A subsequent significance test at 0.05 confirmed that there is a significant (0.00) difference in password strength according to language orientation. Bonneau [START_REF] Bonneau | The Science of Guessing: Analyzing an Anonymized Corpus of 70 Million Passwords[END_REF] observed a loss in efficiency when using a password dictionary that is not related to the language orientation of passwords under test. This could explain our finding given that the password dictionary used is oriented towards English passwords. Nonetheless, this finding confirms that language specifics do have a significant effect on increasing the password search space which positively contributes to password security [START_REF] Bonneau | The Science of Guessing: Analyzing an Anonymized Corpus of 70 Million Passwords[END_REF][START_REF] Voyiatzis | An Empirical Study on the Web Password Strength in Greece[END_REF][START_REF] Ur | I Added '!' at the End to Make It Secure": Observing Password Creation in the Lab[END_REF]. Thus, it can be concluded that a trawling password attack may find native languageoriented passwords difficult to guess. However, findings from this study have to be considered with caution given that users generated passwords using personal information that included names and year of birth as reported in section 4.3 and 4.4. The literature shows that personal information can be exploited to greater effect in a targeted password guessing attack [START_REF] Wang | Targeted Online Password Guessing: An Underestimated Threat[END_REF]. In addition to language orientation, a significant test shows that password length had a significant effect (0.00) on password strength. There is a growing research interest in long passwords (passphrases) with the objectives of improving security and usability [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF].

Recommendations and Conclusion

While passwords found in this study assumed structures that were reported in the literature, namely the LDS or LSD, a closer look at the data showed that a combined character distribution of researched South African and Namibian passwords followed a unique trend compared to that of English passwords. Native languages are considered a major force behind the uniqueness in character distribution. This is an interesting finding given that English is the first written language and a language of instruction in most African countries. Nonetheless, passwords oriented towards the English language were observed in the password corpus. Some of the observed English passwords were based on common English words. Similarly, passwords with an orientation towards native languages were observed in the corpus. The use of different language orientations in password generation reflected common languages within the research context. These findings affirmed the explanation of the psychological development as stated in the socioculture theory. An evaluation to establish the influence of language orientation on password strength showed that passwords based on native languages are significantly stronger than English passwords. No password with a native language orientation was found to be weak or very weak. However, it has to be highlighted that the dominant use of personal information when generating passwords is a huge security concern.

Based on these findings, it is argued that a blacklist with common English oriented passwords can be a useful inclusion in password policies for the targeted multilingual user groups. Findings from this study suggest that such a blacklist has the potential of improving password security on approximately 50% of the occasions. However, a blacklist with English-oriented passwords alone may not be a complete solution when targeting multilingual user groups. For instance, multilingual users can switch between common words in different languages upon stumbling into blocked passwords that are oriented in a particular language. In support of our opinion, [START_REF] Blocki | Optimizing Password Composition Policies[END_REF] concluded "that trivial password choices can vary between contexts, making a simple blacklist approach ineffective" (p. 5). A blacklist with common native names and words could be a useful addition for enhancing password security within the context of this study. However, while a blacklist can enhance security especially in the case of online password attacks, it is vulnerable to offline password attacks and makes password generation complex [START_REF] Shay | Designing Password Policies for Strength and Usability[END_REF][START_REF] Florêncio | An Administrator's Guide to Internet Password Research[END_REF]. It is therefore worthwhile to investigate the usability of blacklists within this study's research context prior to implementation.

In addition, password authentication designers within our study context should consider devising mechanisms that prevent the use of personal information in user generated passwords. Approximately one in three participants based their passwords on names or names of loved ones. Notably, some website platforms are already implementing password generation frameworks that prohibit the use of personal information in passwords [START_REF] De Carnavalet | From Very Weak to Very Strong: Analyzing Password-Strength Meters[END_REF]. In addition, this study confirms that using different language orientations in password generation, within a context, has a potential to increase the password search space which could improve password security. Hence, understanding principles of psychological development according to the socioculture theory could go a long way in guiding users to generate secure and usable passwords. However, there is a need for further research on using native languages in password generation.

Limitations: The study was based on a relatively small sample of 107 when compared to samples reported in the literature. Thus, increasing the sample size could improve the generalisability of findings.

Fig. 1 .

 1 Fig. 1. Password creation strategy.

Fig. 2 .

 2 Fig. 2. Password strength according to zxcvbn.