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In vehicular ad hoc network (VANET), lots of information should be delivered on a large scale in a limited time. Meanwhile, vehicles are quite dynamic with high velocities, which causes a large number of vehicle disconnections. Both of these characteristics lead to unreliable information transmission in VANET. A vehicle clustering algorithm, which organizes vehicles in groups, is introduced in VANET to improve network scalability and connection reliability. However, different clustering techniques and algorithms are required for different scenarios, such as information transmission, routing, and accident detections. This paper explores the vehicle clustering techniques from the aspects of cluster head selection, cluster formation, and cluster maintenance procedures. Meanwhile, context-based clustering algorithms are summarized, and the hybrid-clustering algorithms are highlighted. The paper also summarizes the existing clustering performance metrics and performance evaluation approaches.

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International audience; Biometrics testing has for objective to determine the performance of a biometric system in order to guarantee security and user experience requirements. Providing trust in biometric systems is a key for many manufacturers. The performance is usually measured through the computation of matching scores between legitimate and impostor samples from a given database. Different bias in particular those linked to the environmental conditions can modify the performance of a biometric system. In this paper, we study the impact of acquisition conditions on fingerprint systems considering at the same time the quality and accuracy. We defined an own-made database controlling the acquisition conditions and we observe the behavior of three different matchers on these biometric data. Experimental results allow us to quantify their impact on performance and draw conclusions for testing biometric systems.

International audience; In this paper, we propose a multidimensional trust model for vehicular networks. Our model evaluates the trustworthiness of each vehicle using two main modes: 1) Direct Trust Computation DTC related to a direct connection between source and target nodes, 2) Indirect Trust Computation ITC related to indirectly communication between source and target nodes. The principal characteristics of this model are flexibility and high fault tolerance, thanks to an automatic trust scores assessment. In our extensive simulations, we use Total Cost Rate to affirm the performance of the proposed trust model.

Digital libraries build on classifying contents by capturing their semantics and (optionally) aligning the description with an underlying categorization scheme. This process is usually based on human intervention, either by the content creator or a curator. As such, this procedure is highly time-consuming and - thus - expensive. In order to support the human in data curation, we introduce an annotation tagging system called “AnnoTag”. AnnoTag aims at providing concise content annotations by employing entity-level analytics in order to derive semantic descriptions in the form of tags. In particular, we are generating “Semantic LOD Tags” (linked open data) that allow an interlinking of the derived tags with the LOD cloud. Based on a qualitative evaluation on Web news articles we prove the viability of our approach and the high-quality of the automatically extracted information.

Digital curation requires substantial human expertise in order to achieve and maintain document collections of high quality. This necessitates usually expert knowledge of a librarian or curator in order to interpret the content and categorize it accordingly. This process is at the same time expensive and time-consuming. With the advent of knowledge bases and the plenitude of information contained within them new opportunities emerge at the horizon. In particular, entity-level analytics allows to semantically enrich contents via linked open data (LOD). To this end, we assess in this paper the approach of concise content annotation as a means of supporting the process of digital curation. In particular, we compare various entity-level annotation methods and highlight the importance of concise semantic tagging based on qualitative as well as quantitative evaluations.

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International audience; Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation in Europe. In the case of keystroke dynamics, most existing databases have less than 200 users. For these reasons, it is crucial for this biometric modality to be able to generate a significant and realistic synthetic dataset of keystroke dynamics samples. We propose in this paper an original approach for the generation of synthetic keystroke data given samples from known users as a first step towards the generation of synthetic datasets. Experimental results show the capability of the proposed statistical model to generate realistic samples from existing datasets in the literature.

International audience; Cyber-attacks have spread all over the world to steal information such as trade secrets, intellectual property and banking data. Facing the danger of the insecurity of saved data (personal, professional, official, etc.), keystroke dynamics was proposed as an interesting, non-intrusive, inexpensive, permanent and weakly constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a single-capture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.

Biometric authentication is used to secure digital or physical access. Such an authentication system uses a biometric database, where data are sometimes protected by cancelable transformations. This paper introduces the notion of biometric masterkeys. A masterkey is a feature vector such that the corresponding template matches with a significant number of templates stored in a cancelable biometric database. Such a masterkey is directly researched from a cancelable biometric database, but we also investigate another scenario in which the masterkey is fixed before the creation of the cancelable biometric database, providing additional access rights in the system for the masterkey's owner. Experimental results on the fingerprint database FVC and the face image database LFW show the effectiveness and the efficiency of such masterkeys in both scenarios. In particular, from any given feature vector, we are able to construct a cancelable database, for which the biometric template matches with all the templates of the database.

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International audience; Ce chapitre présente les fondements de la biométrie, de ses origines aux nouveaux défis scientifiques, sociétaux et champs d'applications envisageables dans un futur proche, en passant par les usages actuels que tout un chacun a adoptés, que ce soit pour déverrouiller son téléphone ou pour passer un contrôle aux frontières.

The emergence of Internet of Energy (IoE) paves the way for sustainable and green energy environments that reduce energy costs and integrate Renewable Energy Sources (RESs) as new sources of energy. Electric vehicles (EVs) are one of the main actors of IoE future. The emergence of EVs promises to reduce the environmental crisis (e.g., carbon emissions); however, their charging process will consume massive amounts of electricity and may affect the reliability of the Smart Grid (SG). Recently, vehicle-to-vehicle (V2V) electricity trading approach has gained momentum as a novel strategy that reduces the peak power consumption in SG. In this context, EVs compete to provide electricity with lower prices, while maintaining the V2V electricity trading system secure. However, they lack flexibility, transparency, and authenticity. More importantly, they are based on centralized models (i.e., EV aggregators) which introduce single-point-of-failure and may cause the collapse of the system. In this paper, we propose a fully decentralized blockchain-based system that allows for an automated, fair, and trustworthy V2V electricity trading system; it uses Ethereum’s smart contracts to realize the V2V electricity trading system in a fully distributed, transparent, secure, tamper-proof and trustworthy manner. The proposed system is implemented, tested, and deployed on the Ethereum official test network Ropsten. The experiment results show that the proposed solution achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to new decentralized V2V electricity trading systems in SG.

Vehicular Ad hoc Network (VANET) is a wireless technology dedicated to vehicular communications. VANET is unsecure because of its lack of central administration and is also vulnerable due to its unique characteristics. Therefore, security is one of the most critical issues in this type of network. For a high level of safety, we need a method to evaluate vehicular communications’ trust and measure nodes’ reliability. This article presents a decentralized reputation model, where the activity of each node (i.e., vehicle) is observable for adjacent nodes. Our model enables nodes to detect malicious vehicles and avoids interacting with them. We propose to use the Bayesian filter to measure the trust scores of vehicles accurately. Different from conventional trust models, we study the concept of classification and misclassification. We then analyze the accuracy of our Bayesian filter by computing the factors of Node Recall and Node Precision. The extensive simulations have shown that the proposed filter can assign an accurate trust score to nodes under various network conditions and misbehavior rates.

International audience; As fog computing has developed, new challenges have emerged regarding the placement of services within the network. Such issues are becoming especially important since the integration of IoT infrastructure and the relevant security concerns. There is significant variation in the definitions of service placement problems covered in the literature. Comparing these placements is a difficult task since the services, their placement objectives, their relationships with each other and the infrastructure of the network, strongly contribute to this diversity of definitions. This article proposes a methodology for comparing service placement algorithms and solutions supported by an ontology of service placement problems. This ontology is therefore used in our methodology to solve the problems annotated therein and to compare the results of the various algorithms as well as the quality of their solutions.

Dans le cadre de l'animation scientifique sur les sujets du GT « Sécurité des Systèmes, Logiciels et Réseaux » (SSLR), une journée thématique dédiée à la sécurité des réseaux s'est tenue le 11 mai 2021 en ligne, en partenariat avec le GDR « Réseaux et Systèmes Distribués ».; International audience; Le réseau ad hoc VANET est une technologie sans fil dédiée aux communications véhiculaires. Ces réseaux VANETs ne sont pas faibles et la sécurité doitêtre prise en compte comme l'un des problèmes les plus critiques. Pour renforcer la sécurité, nous proposons une méthode pourévaluer la confiance des communications véhiculaires et mesurer la fiabilité des véhicules. Nous présentons un modèle décentralisé de réputation dans lequel les noeuds représentent des véhicules. L'activité de chaque véhicule est observable par les noeuds adjacents. Notre modèle permet aux noeuds de détecter les véhicules malveillants et d'éviter d'interagir avec eux. Nous proposons d'utiliser un filtre Bayésien pour mesurer avec précision les scores de confiance des véhicules. Nousétudionségalement le concept de classification afin de distinguer le comportement des véhicules. Ensuite, nous analysons la précision de notre filtre bayésien en calculant les différents facteurs. Les simulations approfondies ont montré que le filtre proposé peut attribuer un score de confiance précis aux noeuds dans diverses configurations de réseau.

International audience

International audience; Intelligent transportation systems (ITS) play a central role in improving traffic safety, and managing logistics and transport operations. Thanks to this technology, vehicles are now able to communicate with each other thanks to the V2V (vehicle to vehicle) communication system, and also with the nearby road infrastructure V2I (vehicle to infrastructure) with the aim of reducing the number of road accidents, reducing congestion and hence carbon emissions, and increasing road safety and transport operations efficiency.This special issue aims to gather the last trends and challenges raised in different research communities working on intelligent transportation systems, with a particular focus on the studies dealing with the development of tools issued from Operational Research, Artificial Intelligence, security, and traffic control fields.An upcoming special issue of the journal Information is dedicated to the subjects described before. Interested authors are invited to submit original work to this special issue coordinated by Prof. Nacima Labadie and Prof. Lyes Khoukhi. Topics of interest include, but are not limited to, the following: Intelligent vehicles, Traffic control and management, Modelling, control and simulation algorithms and techniques, Deep learning and Artificial Intelligence, Logistics and supply chain, Smart mobility.

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International audience; The rise of edge computing enables local network management. Services are no longer clustered inside the cloud but rather spread all over the whole network. In this paper, we propose a method for deploying security services within an IoT network according to devices capabilities. Our method models an IoT network as a weighted graph using device capabilities. Using the latter, we propose to identify the most suitable location for a security service using dominating sets and the graph weights. We present results obtained by the proposed method on an example of a smart city based on real data using network security functions such as an IPsec service. Results indicate an overall increase in the network security with minimal impact on the information flow while retaining reduced deployment costs.

The Internet of Things (IoT) has been on the rise for several years now. Following recent attacks on these systems, studies have shown that most of these devices were not sufficiently secured. The ideal solution to this problem would be to provide security mechanisms on all IoT devices however, this solution is not always achievable.An alternative strategy to secure these systems would be to deploy security services at the edge of the network to bring the security mechanisms as close as possible to unsecured devices.The purpose of this thesis is to design an adaptive security framework for IoT devices relying on security services positioning. This work is divided into three contributions, each of which affects different aspects of our approach.The first contribution provides a strategy for deploying security services that minimizes the cost of deployment. This method expresses our positioning constraints into a graph problem which we suggest solving using graph theory.The second contribution formalizes the service placement problems and models them into an ontology. The latter is then used to solve those problems and to compare their different solutions.The third contribution focuses on security services that implement \textit{whitebox} cryptography. In this contribution, we present a mechanism for anchoring these implementations on an IoT network to prevent code lifting attacks and device theft.Finally, we present an adaptive security framework for IoT objects in which we position all the contributions made during this thesis., Depuis maintenant plusieurs années, nous assistons à l'essor de l'Internet des Objets (IdO ou IoT en anglais). Suite à de récentes attaques sur ces systèmes, les études ont démontré que la sécurité de ces appareils était majoritairement insuffisante. Afin de remédier à ce problème, nous devrions idéalement mettre en place des mécanismes de sécurité sur l'ensemble des périphériques IoT, cependant cette solution n'est pas toujours envisageable.Une approche alternative, pour sécuriser ces systèmes, consiste à déployer des services de sécurité en bordure du réseau afin de rapprocher les mécanismes de sécurité au plus près des périphériques non sécurisés.Le but de cette thèse est de constituer un framework de sécurisation adaptative des objets de l'IoT qui repose sur le positionnement de services de sécurité. Ce travail se décompose en trois contributions qui touchent chacune des aspects différents de notre approche.La première contribution élabore une stratégie de déploiement de services de sécurité qui minimise leurs coûts de déploiement. Cette approche traduit nos contraintes de positionnement sous la forme d'un problème de graphes que nous proposons de résoudre à l'aide d'outils de théorie des graphes.La seconde contribution formalise les problèmes de placement de services pour les modéliser sous la forme d'une ontologie. Cette dernière est alors utilisée pour résoudre ces problèmes et permettre de comparer leurs différentes solutions.La troisième contribution se focalise sur les services de sécurité qui implémentent de la cryptographie \textit{whitebox}. Dans cette contribution, nous proposons un mécanisme d'ancrage de ces implémentations sur un réseau IoT afin de prévenir les attaques par extraction de code ainsi que le vol du périphérique.Finalement, nous proposons un framework de sécurisation adaptative des objets de l'IoT dans lequel nous positionnons l'ensemble des contributions réalisées pendant cette thèse.

International audience; In the digital revolution, even secure communication between individuals, services and devices through centralized digital entities presents considerable risks. Service providers collect and store information that is used for data mining, profiling and exploitation without users' knowledge or consent. Having service providers continue to offer their centric solutions is inefficient in terms of duplication, has serious security lacunae and is cumbersome to the users. The Self-sovereign Identity (SSI) concept, which includes the individual's consolidated digital identity and verified attributes, enables the users of data to exert their ownership and gain insights from their data's usage. The authentication and verification of digital identity is essential to achieve the privacy and security of distributed digital identities. However, the current literature still lacks the comprehensive study on components of identity management as well as user privacy and data protection mechanisms in identity management architecture. In this paper, we provide a coherent view of the central concepts of SSI, including the components of identity proofing and authentication solutions for different SSI solutions. Firstly, we discussed an overview of Identity management approaches, introducing an architecture overview as well as the relevant actors in such a system and blockchain technology as solution for distributed user-centric identity. Then we analyzed the authentication and verification mechanisms in the context of digital identity. Finally, we discuss the existing solutions and point out the research gaps and elaborate challenges and trade-offs towards building a complete identity management system (IdMs).

Smartphones have become ubiquitous in everyday life, storing and generating a huge amount of sensitive personal data which makes them vulnerable to increasing security and privacy threats. While protecting smartphones has become a necessity, existing traditional authentication methods, which are mainly based on the use of PIN codes and passwords, are facing remarkable drawbacks. Behavioural biometricsbased authentication is an interesting alternative to ensure a better protection and a better usability. This paper presents a transparent behavioural authentication solution using smartphone calling habits data. tested on a dataset of 93 users with more than 16.000 samples and shows promising results while guarantying a high privacy protection.

International audience; Due to the advantages in privacy and efficiency requirements, minutiae template based matching is the dominant technique among the authentication approaches of fingerprint image and its performance fully relies on the quality of the input fingerprint image. In this case, it is reasonable to consider qualifying fingerprint with fingerprint minutiae template information extracted from fingerprint image, particularly when using for embedded applications due the limited memory. In fact, the speed of fingerprint recognition increases with the decrease of the size of database. For these reasons, a new confidence measure called Minutia Confidence Index (MiCI) for each minutia of the template is proposed. This index predicts the importance and the usefulness of each minutia with respect to the others in the template. It takes into account only minutiae template information (i.e., x and y coordinates, the type and the orientation). MiCI score is a value between 0 and 1, where highest values are for the mostly relevant minutiae in the template whereas lowest values are for less important ones. This measure has been applied in the template reduction use case on Fingerprint Verification Competition (FVC) and SFINGE0 databases and demonstrated its capability to reach high performance.

In this paper, we present a full-reference quality assessment metric based on the information of visual saliency. The saliency information is provided under the form of degrees associated to each vertex of the surface mesh. From these degrees, statistical attributes reflecting the structures of the reference and distorted meshes are computed. These are used by four comparisons functions genetically optimized that quantify the structure differences between a reference and a distorted mesh. We also present a statistical comparison study of six full-reference quality assessment metrics for 3D meshes. We compare the objective metrics results with humans subjective scores of quality considering the 3D meshes in one hand and the distorsion types in the other hand. Also, we show which metrics are statistically superior to their counterparts. For these comparisons we use the Spearman Rank Ordered Correlation Coefficient and the hypothetic test of Student (ttest). To attest the pertinence of the proposed approach, a comparison with a ground truth saliency and an application associated to the assessment of the visual rendering of smoothing algorithms are presented. Experimental results show that the proposed metric is very competitive with the state-of-the-art.

International audience; The quality assessment of biometric samples is a crucial issue in biometrics, indeed, many studies showed its significant impact on the subsequent performance of the biometric system. Many metrics have been proposed and studied in the literature in order to quantify their usefulness. In this paper, we propose to merge different metrics in order to improve the utility estimation of the quality assessment. We use the enrollment selection validation approach in order to compute the utility estimation of the fused metrics. We show the efficiency of the proposed approach comparing with 7 well known metrics on the 12 FVC datasets and 5 synthesized SFinGE-based databases with two matching algorithms. Experimental results show a good improvement on the fused metric to better qualify the quality of digital fingerprints. Those results demonstrate the effectiveness of the approach.

Smart grids (SGs) and advanced metering infrastructures (AMIs) are considered as the new evolution of classical electrical grids. The recent emergence of smart meters is paving the way for the proliferation of smart grids, where billions of smart meters are interconnected to provide novel pervasive services (e.g., real time pricing application and real time energy consumption), and automate diagnostic and daily energy metering (i.e., gas, electric) tasks (e.g., billing, monitoring, planning and predicting of energy usage). The recent explosion in the number of insecure smart meters is changing the view towards SG from enabler of smart homes into a powerful amplifying tool that creates new vectors for cyberattacks (i.e., smart-homes Distributed Denial-of-Service (DDoS) attacks) at large scale. This motivated us to design a new flexible, secure, efficient and trustworthy access control scheme based on blockchain and smart contract. Although access control exists in AMI, it is based on a centralized model (i.e., router/gateway, firewall) which introduces a bottleneck (i.e., single point of failure) and causes the collapse of the system. In this paper, we propose a new decentralized-based access control architecture for SG based on blockchain; it uses smart contracts (i.e., Ethereum’s smart contracts) in order to manage permissions in a fully distributed and trustworthy manner. The architecture is implemented, tested and deployed on the Ethereum official test network Ropsten [1]. The results confirm that the proposed blockchain based access control scheme achieves security, flexibility, efficiency, and cost effectiveness making it a promising solution to mitigate DDoS attacks in SGs.

International audience; Ce chapitre a pour objectif de définir la notion d'identité numérique d'un individu comme représentation numérique de ses informations à caractère personnel sur internet. Après un rappel historique motivant ce besoin, les exigences sécuritaires, d'usage et de protection de la vie privée sont détaillées. La législation sur la protection des données personnelles est une des méthodes contribuant à garantir à l'individu la protection de sa vie privée mais plusieurs technologies peuvent y contribuer comme la cryptographie ou l'apparition de services numériques ayant pour objectif de gérer l'identité numérique d'un individu. L'authentification est une étape clef dans la gestion d'identité numérique pour garantir qu'un individu puisse bénéficier d'un service numérique avec les droits associés. Plusieurs méthodes sont possibles dans la littérature en allant du simple mot de passe à la biométrie. Cette dernière approche se répand de plus en plus sur nos objets numériques (smartphone, tablette...) et présente à la fois des avantages (lien étroit entre l'authentifiant et l'individu) mais aussi des inconvénients qu'il faut limiter (caractère non révocable d'une donnée biométrique par exemple).

International audience; This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it “Deep Biometrics”. The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications.Highlights the impact of deep learning over the field of biometrics in a wide area;Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.;Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

International audience; Distant learning is an alternative solution to education when the learner is far from the school or cannot attendcourses for professional or medical reasons. The main objective of this work is to design a smart applicationof remote exams, using a multibiometric system combining face with deep learning and keystroke dynamicsto verify the identity of the learner. Privacy protection is consider in this work as an important issue becausemany personal data are processed in the proposed solution. We consider in this paper experiments under real-life conditions to identify abnormal behaviours with confidence indicators. We show the system ability tomake the correct decision while preserving learner’s privacy.

International audience; The Möbius transform is a crucial transformation into the Boolean world; it allows to change the Boolean representation between the True Table and Algebraic Normal Form. In this work, we introduce a new algebraic point of view of this transformation based on the polynomial form of Boolean functions. It appears that we can perform a new notion: the Möbius computation variable by variable and new computation properties. As a consequence, we propose new algorithms which can produce a huge speed up of the Möbius computation for sub-families of Boolean function. Furthermore we compute directly the Möbius transformation of some particular Boolean functions. Finally, we show that for some of them the Hamming weight is directly related to the algebraic degree of specific factors.

At the era where our digital identity is always more melt with our personal identity, the needs in security of our online accounts are even more pronounced. Passwords are both the most used authentication mean and the weakest link in the security chain. Despite the undeniable weakness of online passwords, they remain the best authentication mean gathering security, accessibility and privacy protection.The purpose of this thesis is to ease the design of passwords strength measurement methods relevant regarding the most sophisticated attacks on passwords. Such attacks lay on probabilistic models which model the way that passwords are chosen. These attacks are very efficient to find more complex passwords that are usually not found by naive techniques. This work lay on three contributions to spot the key aspects of a modern password strength measurement method. The first contribution models the attack process on passwords, formalizes and measures the performance of such a process. The second contribution shows that currently deployed strength measurement techniques lack to protect passwords against sophisticated attacks. The third contribution analyses the algorithms of sophisticated attacks by observing their behaviour to design techniques that increase the execution cost of these attacks. Validating the methods has been done by using passwords from publicly disclosed leaks, for a total of more than 500 millions passwords., À l'ère où notre identité numérique se confond toujours davantage avec notre identité personnelle, les besoins en sécurité de nos comptes en ligne sont d'autant plus marqués. Les mots de passe sont à la fois la manière de s'authentifier la plus utilisée et à la fois le maillon le plus faible de la chaîne de sécurité. Malgré l'indéniable fragilité de la plupart des mots de passe utilisés en ligne, le mot de passe reste le meilleur moyen de s'authentifier réunissant sécurité, accessibilité et respect de la vie privée.L'objectif de cette thèse est de faciliter la conception de mesures de robustesse des mots de passe qui soient pertinentes vis-à-vis des attaques les plus sophistiquées sur les mots de passe. Ces attaques reposent sur des modèles probabilistes de la manière dont les utilisateurs choisissent leur mot de passe. Il s'avèrent que ces attaques sont très efficaces pour trouver des mots de passe plus complexes que ceux habituellement trouvés par les méthodes naïves. Ce travail repose sur trois contributions pour identifier les points clés d'une mesure de robustesse des mots de passe moderne. La première contribution modélise le processus d'attaque sur les mots de passe, formalise et mesure la performance d'un tel processus. La deuxième contribution se charge de montrer que les méthodes actuellement déployées pour mesurer la robustesse des mots de passe ne sont pas suffisantes pour se prémunir des attaques sophistiquées. La troisième contribution analyse algorithmiquement les attaques sophistiquées en observant leur comportement dans le but de concevoir des méthodes efficaces pour augmenter le coût d'exécution de ces attaques. La validation des méthodes s'est effectuée en utilisant des mots de passe issus de fuites de données publiques, totalisant plus de 500 millions de mots de passe.

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International audience; In this paper, we propose a new method for optimizing the deployment of security solutions within an IoT network. Our approach uses dominating sets and centrality metrics to propose an IoT security framework where security functions are optimally deployed among devices. An example of such a solution is presented based on EndToEnd like encryption. The results reveal overall increased security within the network with minimal impact on the traffic.

International audience; Nowadays, there are large country-sized fingerprint databases for identification purposes, for border access controls and also for Visa issuance procedures around the world. Fingerprint indexing techniques aim to speed up the research process in automatic fingerprint identification systems. Therefore, several preselection, classification and indexing techniques have been proposed in the literature. However, the proposed systems have been evaluated with different experimental protocols, that makes it difficult to assess their performances. The main objective of this paper is to provide a comparative study of fingerprint indexing methods using a common experimental protocol. Four fingerprint indexing methods, using naive, cascade, matcher and Minutiae Cylinder Code (MCC) approaches are evaluated on FVC databases from the Fingerprint Verification Competition (FVC) using the Cumulative Matches Curve (CMC) and for the first time using also the computing time required. Our study shows that MCC gives the best compromise between identification accuracy and computation time.

International audience; Many studies propose strong user authentication based on biometric modalities. However, they often either, assume a trusted component, are modality-dependant, use only one biometric modality, are reversible , or does not enable the service to adapt the security on-the-fly. A recent work [1] introduced the concept of Personal Identity Code Respecting Privacy (PICRP), a non-cryptographic and non-reversible signature computed from any arbitrary information. In this paper, we extend this concept with the use of Keystroke Dynamics, IP and GPS geo-location by optimizing the pre-processing and merging of collected information. We demonstrate the performance of the proposed approach through experimental results and we present an example of its usage.

International audience; The attacks considered for keystroke dynamics study especially adaptive strategies have commonly treated impersonation attempts known as zero-effort attacks. These attacks are generally the acquisition of other users of the same database while typing the same password without intending to impersonate the genuine user account. To deal with more realistic scenarios, we are interested in this paper to study the robustness of an adaptive strategy against four types of imposter attacks: zero-effort, spoof, playback and synthetic applied to the WEBGREYC database. Experimental results show that 1) playback and synthetic attacks are the most dangerous and increase the EER rates compared to the other attacks;2) we also find that the impact of these attacks is more pronounced when the percentages of imposter samples are greater than those of genuine ones;3) the spoof attacks achieve alarmingly higher FMR, FNMR, and EER rates compared to zero-effort impostor attacks; 4) FMR, FNMR, and EER are higher when the percentage of attacks increases; 5) the attacks belonging to the same user are more dangerous than those of different users in particular when the percentage of the attacks increases. In light of our results, we point out that the traditional attacks considered in research on keystroke-based authentication must evolve according to the evolution of the attacks of nowadays password-based applications.

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

International audience; In this work, we aim to measure the impact of hash functions on the password cracking process. This brings us to measure the performance of password enumerators, how many passwords they find in a given period of time. We propose a performance measurement methodology for enumerators, which integrates the success rate and the speed of the whole password cracking process. This performance measurement required us to develop advanced techniques to solve measurement challenges that were not mentioned before. The experiments we conduct show that software-optimized enumerators like John The Ripper-Markov and the bruteforce perform well when attacking fast hash functions like SHA-1. Whereas enumerators like OMEN and PCFG-based algorithm perform the best when attacking slow hash functions like bcrypt or Ar-gon2. Using this approach, we realize a more in-depth measurement of the enumerators performance, considering quantitatively the trade-off between the enumerator choice and the speed of the hash function. We conclude that software-optimized enumerators and tools must implement academic methods in the future.

Wireless mesh networks (WMNs) have become an increasingly popular wireless networking technology for establishing the last-mile connectivity for home and neighbourhood networkings. In such networks, packet dropping may be due to either an attack, or normal loss events such as bad channel quality. Furthermore, in the route discovery phase, path stability is not always considered. We consider a special case of denial of service (DoS) attack in WMNs known as the greyhole attack. In this attack, a node selectively drops some packets which it has to forward along the path. To mitigate this attack, we propose a dropping detection mechanism allowing a mobile node to select a most reliable route to the destination. Our detection module detects misbehaving nodes by comparing the observed packet loss distribution of nodes to the expected ones when they are well-behaved. We validate the proposed approach via extensive simulations through R software and Matlab.

National audience

16th Int.l Summer School on Biometrics and Forensics 2019; International audience; Keystroke Dynamics enables the authentication or identification of users by analyzing their way of typing, e.g. when browsing the Internet. Most studies in the state of the art focus on increasing Keystroke Dynamics Systems performances. In this paper, we address the problematic of avoiding biometric authentication with keystroke dynamics in order to protect users' privacy against unwanted authentication/identification. Experimental results obtained on significant datasets show the benefits of the proposed approaches.