Context. The potential to track individuals become greater and greater in the society today. We want to develop a method that is easy to understand so more people can participate in the discussion about the collection, and storing, of seemingly non-invasive device data and personal integrity. Objectives. In this work we investigate the potential to connect a WiFi enabled device to a known individual by analysing log files. Since we want to keep the method as simple as possible we choose to not use machine learning because this might add unnecessary layers of complexity. Methods. The conducted experiments were performed against a test group consisting of six persons. The dataset used consisted of authentication logs from a university WiFi-network collected during a month and data acquired by capturing WiFi-traffic. Results. We were able to connect 67% of the targeted test persons to their smart phones and 60% to their laptops. Conclusions. In this work we conclude that a device identifier in combination with data that can tie it to a location at a given time is to be seen as sensitive information with regard to personal integrity. We also conclude that it is possible to create and use an easy method to connect a device to a given person.

Mobile applications are software that extend the functionality of our smartphones by connecting us with friends and a wide range of other services. Android, which is an operating system based on the Linux kernel, leads the market with over 2.6 million applications recorded on their official store. Application developers, due to the ever-growing innovation in smartphones, are compelled to release new ideas on limited budget and time, resulting in the deployment of malicious applications. Although there exists a security mechanism on the Google Play Store to remove these applications, studies have shown that most of the applications on the app store compromise privacy or pose security-related risks. It is therefore essential to investigate the security risk of installing any of these applications on a device. The objectives are to identify methods and techniques for assessing mobile application security, investigate how attributes indicate the harmfulness of applications, and evaluate the performance of K Nearest Neighbors(K-NN) and Random forest machine learning models in assessing the security risk of installing mobile applications based on information available on the application distribution platform. A literature analysis was done to gather information on the different methods and techniques for assessing security in mobile applications and investigations on how different attributes on the application distribution platform indicate the harmfulness of an application. An experiment was also conducted to examine how various machine learning models perform in evaluating the security risk associated with installing applications, based on information on the application distribution platform. Literature analysis presents the various methods and techniques for mobile application security assessment and identifies how mobile application attributes indicate the harmfulness of mobile applications. The experimental results demonstrate the performance of the aforementioned machine learning models in evaluating the security risk of installing mobile applications. In conclusion, Static, dynamic, and grey-box analysis are the methods used to evaluate mobile application security, and machine learning models including K-NN and Random forest are suitable techniques for evaluating mobile application security risk. Attributes such as the permissions, number of installations, and ratings reveal the likelihood and impact of an underlying security threat. The K-NN and Random forest models when compared to evaluate the security risk of installing mobile applications based on information on the application distribution platform showed high performance with little differences.

The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data' requirement. This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas: Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems. Community Detection: Finding communities from multiple sources of information. Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks.

Made available in DSpace on 2014-12-17T14:55:40Z (GMT). No. of bitstreams: 1 JoseMF.pdf: 585000 bytes, checksum: d743090da952a3d8b178ffb4048abd4b (MD5) Previous issue date: 2009-12-16 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / RFID (Radio Frequency Identification) identifies object by using the radio frequency which is a non-contact automatic identification technique. This technology has shown its powerful practical value and potential in the field of manufacturing, retailing, logistics and hospital automation. Unfortunately, the key problem that impacts the application of RFID system is the security of the information. Recently, researchers have demonstrated solutions to security threats in RFID technology. Among these solutions are several key management protocols. This master dissertations presents a performance evaluation of Neural Cryptography and Diffie-Hellman protocols in RFID systems. For this, we measure the processing time inherent in these protocols. The tests was developed on FPGA (Field-Programmable Gate Array) platform with Nios IIr embedded processor. The research methodology is based on the aggregation of knowledge to development of new RFID systems through a comparative analysis between these two protocols. The main contributions of this work are: performance evaluation of protocols (Diffie-Hellman encryption and Neural) on embedded platform and a survey on RFID security threats. According to the results the Diffie-Hellman key agreement protocol is more suitable for RFID systems / Identifica??o por r?dio freq??ncia, tamb?m chamada de RFID (Radio Frequency Identification), representa uma tecnologia de transmiss?o de dados sem fio. Estes dados s?o relacionados principalmente a c?digos de identifica??o. A tecnologia RFID vem apresentando um grande potencial de utiliza??o em setores da automa??o industrial, residencial e hospitalar. No entanto, estas aplica??es podem resultar em riscos a seguran?a e privacidade dos usu?rios. Recentemente, pesquisadores v?m apresentando poss?veis solu??es as amea?as de seguran?a da tecnologia. Entre estas solu??es est?o os protocolos de distribui??o de chaves criptogr?ficas. O presente trabalho tem como objetivo realizar uma avalia??o de desempenho dos protocolos de Criptografia Neural e Diffie-Hellman na gera??o de chaves em sistemas RFID. Para isso, iremos mensurar o tempo de processamento destes protocolos. Para os testes foi desenvolvido uma plataforma em FPGA (Field-Programmable Gate Array) com o processador embarcado Nios IIr. Sobre esta plataforma foram utilizados os protocolos de Criptografia Neural e Diffie-Hellman no processo de gera??o de chaves criptogr?ficas. A metodologia de pesquisa baseia-se na agrega??o de conhecimento ao desenvolvimento de novos sistemas RFID atrav?s de uma an?lise comparativa entre esses dois protocolos de seguran?a da informa??o. As principais contribui??es deste trabalho s?o: avalia??o de desempenho dos protocolos (Diffie- Hellman e Criptografia Neural) em uma plataforma embarcada e um levantamento bibliogr?fico de pesquisas relacionadas ? seguran?a da informa??o em sistemas RFID. Nos resultados obtidos foi poss?vel observar que o protocolo de Diffie-Hellman ? mais apropriado para sistemas RFID

Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users’ fine-grained metering data measured by users’ Smart Meters (SMs). The SEV charging application allows EVs’ charging parameters to be changed depending on the grid’s state in return for incentives for the EV owners. However, both applications impose risks on users’ privacy. Entities having access to users’ fine-grained metering data may use such data to infer individual users’ personal habits. In addition, users’ private information such as users’/EVs’ identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users’ whereabouts, thus breach their privacy. This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner. First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users’ data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases. Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users’ fine-grained operational metering data while preserving users’ privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users’ privacy with minimum performance penalty, users’ metering data are classified and aggregated accordingly by their respective local gateways based on the users’ locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work. Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user’s accountable data, (ii) this register is updated by design at a low frequency, (iii) the user’s supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads. Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charging at home location. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user’s contracted supplier) which is anonymously signed by the user’s private key. This protocol protects the user’s identity privacy from other suppliers as well as the user’s privacy of location from its own supplier. Further, it allows the user’s contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multi-user EV charging is supported and different legitimate EV users (e.g., family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the same EV. On an application level, REVCBAMU supports fair user billing, i.e., each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers. The protocol has been analysed in terms of security and computational overheads.

Malgré les avantages économiques de l’informatique en nuage (ou cloud computing) pour les entreprises et ses multiples applications envisagées, il subsiste encore des obstacles pour son adoption à grande échelle. La sécurité des données sauvegardées et traitées dans le nuage arrive en tête des préoccupations des décideurs des directions des systèmes d'information. De ce fait, l'objectif principal de nos travaux de recherche lors de cette thèse de doctorat est de poser des bases solides pour une utilisation sûre et sécurisée du nuage. Dans un premier lieu, l’externalisation des processus métiers vers le nuage permet aux entreprises de réduire les couts d’investissement et de maitriser les couts d’exploitation de leurs systèmes d’information ; Elle permet aussi de promouvoir la réutilisation des parties (ou fragments) de ses processus métiers en tant que service cloud, éventuellement par des concurrents directs, afin de faciliter le développement de nouvelles applications orientés services ‘SOA’, ainsi la collaboration à l’échelle du nuage. Néanmoins, le fait de révéler la provenance d’un fragment réutilisé est considérée comme une brèche dans la vie privée et risque d’être dommageable pour l’entreprise propriétaire de ce fragment. Les techniques d’anonymisation des données ont fait leurs preuves dans le domaine des bases de données. Notre principale contribution dans cette partie est la proposition d’un protocole basée sur l’anonymisation des fragments de processus métiers afin de garantir à la fois, la vie privée de leurs propriétaires et la disponibilité de ces fragments pouvant être réutilisés dans le nuage. Les systèmes d’authentification biométriques permettent une authentification des individus avec une garantit suffisante. Néanmoins, le besoin en ressources informatiques ‘calcul et stockage’ de ces systèmes et le manque de compétences au sein des organismes freinent considérablement leurs utilisations à grande échelle. Le nuage offre la possibilité d’externaliser à la fois le calcul et le stockage des données biométriques à moindre cout et de proposer une authentification biométrique en tant que service. Aussi, l’élasticité du nuage permet de répondre aux pics des demandes d’authentifications aux heures de pointes. Cependant, des problèmes de sécurité et de confidentialité des données biométriques sensibles se posent, et par conséquent doivent être traité afin de convaincre les institutions et organismes à utiliser des fragments externes d'authentification biométriques dans leurs processus métiers. Notre principale contribution dans cette partie est un protocole léger ‘coté client’ pour une externalisation (sur un server distant) de la comparaison des données biométriques sans révéler des informations qui faciliteraient une usurpation d’identité par des adversaires. Le protocole utilise une cryptographie légère basée sur des algorithmes de hachage et la méthode de 'groupe de tests combinatoires', permettant une comparaison approximative entre deux données biométriques. Dans la dernière partie, nous avons proposé un protocole sécurisé permettant la mutualisation d’un Hyperviseur (Outil permettant la corrélation et la gestion des événements issus du SI) hébergé dans le nuage entre plusieurs utilisateurs. La solution proposée utilise à la fois, le chiffrement homomorphique et la réécriture de règles de corrélation afin de garantir la confidentialité les évènements provenant des SI des différents utilisateurs. Cette thèse a été réalisée à l'Université Paris Descartes (groupe de recherche diNo du LIPADE) avec le soutien de la société SOMONE et l'ANRT dans le cadre d'une convention CIFRE. / Cloud computing has become one of the fastest growing segments of the IT industry. In such open distributed computing environments, security is of paramount concern. This thesis aims at developing protocols and techniques for private and reliable outsourcing of design and compute-intensive tasks on cloud computing infrastructures. The thesis enables clients with limited processing capabilities to use the dynamic, cost-effective and powerful cloud computing resources, while having guarantees that their confidential data and services, and the results of their computations, will not be compromised by untrusted cloud service providers. The thesis contributes to the general area of cloud computing security by working in three directions. First, the design by selection is a new capability that permits the design of business processes by reusing some fragments in the cloud. For this purpose, we propose an anonymization-based protocol to secure the design of business processes by hiding the provenance of reused fragments. Second, we study two di_erent cases of fragments' sharing : biometric authentication and complex event processing. For this purpose, we propose techniques where the client would only do work which is linear in the size of its inputs, and the cloud bears all of the super-linear computational burden. Moreover, the cloud computational burden would have the same time complexity as the best known solution to the problem being outsourced. This prevents achieving secure outsourcing by placing a huge additional overhead on the cloud servers. This thesis has been carried out in Université Paris Descartes (LIPADE - diNo research group) and in collaboration with SOMONE under a Cifre contract. The convergence of the research fields of those teams led to the development of this manuscrit.

The increase in cybercrime and cyber-warfare has spurred the cat-and-mouse game of finding and attacking vulnerable devices on government or private company networks. The devices attacked are often forgotten computers that run operating systems with known exploits. Finding these devices are crucial for both an attacker and defender since they may be the only weak link on the network. Device discovery on a network using probing or active fingerprinting methods results in extra traffic on the network, which may strain fragile networks and generates suspect traffic that may get flagged as intrusive. Using passive OS fingerprinting allows an actor to listen in and classify active devices on a network. This thesis shows the features that can be exploited for OS fingerprinting and discusses the importance of TLS payload and time-based features. We also present a data collection strategy that could be utilized for simulating multiple OSs and collecting new datasets. We found that the TLS attributes such as cipher suites play an important role in distinguishing between OS versions.

<p dir="ltr">To assess the security of mobile devices, I begin by identifying the key entities involved in their operation: the user, the mobile device, and the service or device being accessed. Users rely on mobile devices to interact with services and perform essential tasks. These devices act as gateways, enabling communication between the user and the back-end services. For example, a user may access their bank account via a banking app on their mobile device, which communicates with the bank’s back-end server. In such scenarios, the server must authenticate the user to ensure only authorized individuals can access sensitive information. However, beyond user authentication, it is crucial for connected services and devices to verify the integrity of the mobile device itself. A compromised mobile device can have severe consequences for both the user and the services involved.</p><p dir="ltr">My research focuses on examining the methods used by various entities to attest and verify the integrity of mobile devices. I conduct a comprehensive analysis of mobile device attestation from multiple perspectives. Specifically, I investigate how attestation is carried out by back-end servers of mobile apps, IoT devices controlled by mobile companion apps, and large language models (LLMs) accessed via mobile apps.</p><p dir="ltr">In the first case, back-end servers of mobile apps must attest to the integrity of the device to protect against tampered apps and devices, which could lead to financial loss, data breaches, or intellectual property theft. For instance, a music streaming service must implement strong security measures to verify the device’s integrity before transmitting sensitive content to prevent data leakage or unauthorized access.</p><p dir="ltr">In the second case, IoT devices must ensure they are communicating with legitimate companion apps running on attested mobile devices. Failure to enforce proper attestation for IoT companion apps can expose these devices to malicious attacks. An attacker could inject malicious code into an IoT device, potentially causing physical damage to the device or its surroundings, or even seizing control of the device, leading to critical safety risks, property damage, or harm to human lives.</p><p dir="ltr">Finally, in the third case, malicious apps can exploit prompt injection attacks against LLMs, leading to data leaks or unauthorized access to APIs and services offered by the LLM. These scenarios underscore the importance of secure and attested communication between mobile devices and the services they interact with.</p>

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.

<p>Bluetooth is the de facto standard for short-range wireless communications. Besides Bluetooth Classic (BC), Bluetooth also consists of Bluetooth Low Energy (BLE) and Bluetooth Mesh (Mesh), two relatively new protocols, paving the way for its domination in the era of IoT and 5G. Meanwhile, attacks against Bluetooth, such as BlueBorne, BleedingBit, KNOB, BIAS, and BThack, have been booming in the past few years, impacting the security and privacy of billions of devices. These attacks exploit both design issues in the Bluetooth specification and vulnerabilities of its implementations, allowing for privilege escalation, remote code execution, breaking cryptography, spoofing, device tracking, etc.</p> <p><br></p> <p>To secure Bluetooth, researchers have proposed different approaches for both Bluetooth specification (e.g., formal analysis) and implementation (e.g., fuzzing). However, existing analyses of the Bluetooth specification and implementations are either done manually, or the automatic approaches only cover a small part of the targets. As a consequence, current research is far from complete in securing Bluetooth.</p> <p><br></p> <p>Therefore, in this dissertation, we propose the following research to provide missing pieces in prior research toward completing Bluetooth security research in terms of both Bluetooth specification and implementations. (i) For Bluetooth security at the specification level, we start from one protocol in Bluetooth, BLE, and focus on the previously unexplored reconnection procedure of two paired BLE devices. We conduct a formal analysis of this procedure defined in the BLE specification to provide security guarantees and identify new vulnerabilities that allow spoofing attacks. (ii) Besides BLE, we then formally verify other security-critical protocols in all Bluetooth protocols (BC, BLE, and Mesh). We provide a comprehensive formal analysis by covering the aspects that prior research fails to include (i.e., all possible combinations of protocols and protocol configurations) and considering a more realistic attacker model (i.e., semi-compromised device). With this model, we are able to rediscover five known vulnerabilities and reveal two new issues that affect BC/BLE dual-stack devices and Mesh devices, respectively. (iii) In addition to the formal analysis of specification security, we propose and build a comprehensive formal model to analyze Bluetooth privacy (i.e., device untraceability) at the specification level. In this model, we convert device untraceability into a reachability problem so that it can be verified using existing tools without introducing false results. We discover four new issues allowed in the specification that can lead to eight device tracking attacks. We also evaluate these attacks on 13 Bluetooth implementations and find that all of them are affected by at least two issues. (iv) At the implementation level, we improve Bluetooth security by debloating (i.e., removing code) Bluetooth stack implementations, which differs from prior automatic approaches, such as fuzzing. We keep only the code of needed functionality by a user and minimize their Bluetooth attack surface by removing unneeded Bluetooth features in both the host stack code and the firmware. Through debloating, we can remove 20 known CVEs and prevent a wide range of attacks again Bluetooth. With the research presented in this thesis, we improve Bluetooth security and privacy at both the specification and implementation levels.</p>