Depuis les années 90, les attaques par canaux auxiliaires ont remis en cause le niveau de sécurité des algorithmes cryptographiques sur des composants embarqués. En effet, tout composant électronique produit des émanations physiques, telles que le rayonnement électromagnétique, la consommation de courant ou encore le temps d’exécution du calcul. Or il se trouve que ces émanations portent de l’information sur l’évolution de l’état interne. On parle donc de canal auxiliaire, car celui-ci permet à un attaquant avisé de retrouver des secrets cachés dans le composant par l’analyse de la « fuite » involontaire. Cette thèse présente d’une part deux nouvelles attaques ciblant la multiplication modulaire permettant d’attaquer des algorithmes cryptographiques protégés et d’autre part une démonstration formelle du niveau de sécurité d’une contre-mesure. La première attaque vise la multiplication scalaire sur les courbes elliptiques implémentée de façon régulière avec un masquage du scalaire. Cette attaque utilise une unique acquisition sur le composant visé et quelques acquisitions sur un composant similaire pour retrouver le scalaire entier. Une fuite horizontale durant la multiplication de grands nombres a été découverte et permet la détection et la correction d’erreurs afin de retrouver tous les bits du scalaire. La seconde attaque exploite une fuite due à la soustraction conditionnelle finale dans la multiplication modulaire de Montgomery. Une étude statistique de ces soustractions permet de remonter à l’enchaînement des multiplications ce qui met en échec un algorithme régulier dont les données d’entrée sont inconnues et masquées. Pour finir, nous avons prouvé formellement le niveau de sécurité de la contre-mesure contre les attaques par fautes du premier ordre nommée extension modulaire appliquée aux courbes elliptiques. / : Since the 1990s, side channel attacks have challenged the security level of cryptographic algorithms on embedded devices. Indeed, each electronic component produces physical emanations, such as the electromagnetic radiation, the power consumption or the execution time. Besides, these emanations reveal some information on the internal state of the computation. A wise attacker can retrieve secret data in the embedded device using the analyzes of the involuntary “leakage”, that is side channel attacks. This thesis focuses on the security evaluation of asymmetric cryptographic algorithm such as RSA and ECC. In these algorithms, the main leakages are observed on the modular multiplication. This thesis presents two attacks targeting the modular multiplication in protected algorithms, and a formal demonstration of security level of a countermeasure named modular extension. A first attack is against scalar multiplication on elliptic curve implemented with a regular algorithm and scalar blinding. This attack uses a unique acquisition on the targeted device and few acquisitionson another similar device to retrieve the whole scalar. A horizontal leakage during the modular multiplication over large numbers allows to detect and correct easily an error bit in the scalar. A second attack exploits the final subtraction at the end of Montgomery modular multiplication. By studying the dependency of consecutive multiplications, we can exploit the information of presence or absence of final subtraction in order to defeat two protections : regular algorithm and blinding input values. Finally, we prove formally the security level of modular extension against first order fault attacks applied on elliptic curves cryptography.

Les services Web permettent la communication de systèmes hétérogènes sur le Web. Ces facilités font que ces services sont particulièrement adaptés au déploiement dans le cloud. Les efforts de formalisation et de vérification permettent d’améliorer la confiance dans les services Web, néanmoins des problèmes tels que la haute disponibilité et la sécurité ne sont pas entièrement pris en compte. Par ailleurs, les services Web déployés dans une infrastructure cloud héritent des vulnérabilités de cette dernière. En raison de cette limitation, ils peuvent être incapables d’exécuter parfaitement leurs tâches. Dans cette thèse, nous pensons qu’une bonne tolérance nécessite un monitoring constant et des mécanismes de réaction fiables. Nous avons donc proposé une nouvelle méthodologie de monitoring tenant compte des risques auxquels peuvent être confrontés nos services. Pour mettre en oeuvre cette méthodologie, nous avons d’abord développé une méthode de tolérance aux attaques qui s’appuie sur la diversification au niveau modèle. On définit un modèle du système puis on dérive des variantes fonctionnellement équivalents qui remplaceront ce dernier en cas d’attaque. Pour ne pas dériver manuellement les variants et pour augmenter le niveau de diversification nous avons proposé une deuxième méthode complémentaire. Cette dernière consiste toujours à avoir des variants de nos services; mais contrairement à la première méthode, nous proposons un modèle unique avec des implantations différentes tant au niveau des interfaces, du langage qu’au niveau des exécutables. Par ailleurs, pour détecter les attaques internes, nous avons proposé un mécanisme de détection et de réaction basé sur la reflexivité. Lorsque le programme tourne, nous l’analysons pour détecter les exécutions malveillantes. Comme contremesure, on génère de nouvelles implantations en utilisant la reflexivité. Pour finir, nous avons étendu notre environnement formel et outillé de services Web en y incorporant de manière cohérente tous ces mécanismes. L’idée est de pouvoir combiner ces différentes méthodes afin de tirer profit des avantages de chacune d’elle. Nous avons validé toute cette approche par des expériences réalistes. / Web services allow the communication of heterogeneous systems on the Web. These facilities make them particularly suitable for deploying in the cloud. Although research on formalization and verification has improved trust in Web services, issues such as high availability and security are not fully addressed. In addition, Web services deployed in cloud infrastructures inherit their vulnerabilities. Because of this limitation, they may be unable to perform their tasks perfectly. In this thesis, we claim that a good tolerance requires attack detection and continuous monitoring on the one hand; and reliable reaction mechanisms on the other hand. We therefore proposed a new formal monitoring methodology that takes into account the risks that our services may face. To implement this methodology, we first developed an approach of attack tolerance that leverages model-level diversity. We define a model of the system and derive more robust functionally equivalent variants that can replace the first one in case of attack. To avoid manually deriving the variants and to increase the level of diversity, we proposed a second complementary approach. The latter always consists in having different variants of our services; but unlike the first, we have a single model and the implementations differ at the language, source code and binaries levels. Moreover, to ensure detection of insider attacks, we investigated a new detection and reaction mechanism based on software reflection. While the program is running, we analyze the methods to detect malicious executions. When these malicious activities are detected, using reflection again, new efficient implementations are generated as countermeasure. Finally, we extended a formal Web service testing framework by incorporating all these complementary mechanisms in order to take advantage of the benefits of each of them. We validated our approach with realistic experiments.

Users and providers benefit considerably from public Wi-Fi hotspots. Users receive wireless Internet access and providers draw new prospective customers. While users are able to enjoy the ease of Wi-Fi Internet hotspot networks in public more conveniently, they are more susceptible to a particular type of fraud and identify theft, referred to as evil twin attack (ETA). Through setting up an ETA, an attacker can intercept sensitive data such as passwords or credit card information by snooping into the communication links. Since the objective of free open (unencrypted) public Wi-Fi hotspots is to provide ease of accessibility and to entice customers, no security mechanisms are in place. The public’s lack of awareness of the security threat posed by free open public Wi-Fi hotspots makes this problem even more heinous. Client-side systems to help wireless users detect and protect themselves from evil twin attacks in public Wi-Fi hotspots are in great need. In this dissertation report, the author explored the problem of the need for client-side detection systems that will allow wireless users to help protect their data from evil twin attacks while using free open public Wi-Fi. The client-side evil twin attack detection system constructed as part of this dissertation linked the gap between the need for wireless security in free open public Wi-Fi hotspots and limitations in existing client-side evil twin attack detection solutions. Based on design science research (DSR) literature, Hevner’s seven guidelines of DSR, Peffer’s design science research methodology (DSRM), Gregor’s IS design theory, and Hossen & Wenyuan’s (2014) study evaluation methodology, the author developed design principles, procedures and specifications to guide the construction, implementation, and evaluation of a prototype client-side evil twin attack detection artifact. The client-side evil twin attack detection system was evaluated in a hotel public Wi-Fi environment. The goal of this research was to develop a more effective, efficient, and practical client-side detection system for wireless users to independently detect and protect themselves from mobile evil twin attacks while using free open public Wi-Fi hotspots. The experimental results showed that client-side evil twin attack detection system can effectively detect and protect users from mobile evil twin AP attacks in public Wi-Fi hotspots in various real-world scenarios despite time delay caused by many factors.

In the novel The Stringer, Perla Coughlin has evacuated Pittsburgh following a biological attack. Now, at a Red Cross Shelter in Ohio, she slowly strings a racket as she tells a volunteer about her past, Pittsburgh's past, and what might lay ahead for both. Characters grapple with their ignorance toward environmental warning signs, the hidden costs of technology, and the disquieting ways in which apocalypses reveal "who we really are." The first-person novel relies heavily on the epistolary tradition as well as Oulipean conventions of form and structure. Tennis serves as a unifying force amongst the four main characters, and the 2010 U.S. Census provides the vehicle through which terrorists launch their plot. The novel aims at a return toward Pittsburgh, but returning home is easier said that done. The city and its industries have been fundamentally changed. Personal secrets have been brought to light. Perla's fierce loyalty to her city makes forgiveness as tall as task as clawing her way back to Pittsburgh.

Doctor Educationis / Cybersecurity defense tools, techniques and methodologies are constantly faced with increasing challenges including the evolution of highly intelligent and powerful new generation threats. The main challenges posed by these modern digital multi-vector attacks is their ability to adapt with machine learning. Research shows that many existing defense systems fail to provide adequate protection against these latest threats. Hence, there is an ever-growing need for self-learning technologies that can autonomously adjust according to the behaviour and patterns of the offensive actors and systems. The accuracy and effectiveness of existing methods are dependent on decision making and manual input by human expert. This dependence causes 1) administration overhead, 2) variable and potentially limited accuracy and 3) delayed response time. In this thesis, Autonomous Threat Detection and Response (ATDR) is a proposed general method aimed at contributing toward security related self-protected networks. Through a combination of unsupervised machine learning and Deep learning, ATDR is designed as an intelligent and autonomous decision-making system that uses big data processing requirements and data frame pattern identification layers to learn sequences of patterns and derive real-time data formations. This system enhances threat detection and response capabilities, accuracy and speed. Research provided a solid foundation for the proposed method around the scope of existing methods and the unanimous problem statements and findings by other authors.

Major cyber attacks against the cyber networks of organizations has become a common phenomenon nowadays. Cyber attacks are carried out both through the spread of malware and also through multi-stage attacks known as hacking. A cyber network can be represented directly as a simple directed or undirected network (graph) of nodes and arcs. It can also be represented by a transformed network such as the attack graph which uses information about network topology, attacker profile, and existing vulnerabilities to represent all the potential attack paths from readily accesible vulnerabilities to valuable target nodes. Then, interdicting or hardening a subset of arcs in the network naturally maps into deploying security countermeasures on the associated devices or connections. In this dissertation, we develop network interdiction models and algorithms to optimally select a subset of arcs which upon interdiction minimizes the spread of infection or minimizes the loss from multi-stage attacks. In particular, we define four novel network connectivity-based metrics and develop interdiction models to optimize the metrics. Direct network representation of the physical cyber network is used as the underlying network in this case. Two of the interdiction models prove to be very effective arc removal methods for minimizing the spread of infection. We also develop multi-level network interdiction models that remove a subset of arcs to minimize the loss from multi-stage attacks. Our models capture the defenderattacker interaction in terms of stackelberg zero-sum games considering the attacker both as a complete rational and bounded rational agents. Our novel solution algorithms based on constraint and column generation and enhanced by heuristic methods efficiently solve the difficult multi-level mixed-integer programs with integer variables in all levels in reasonable times.

Different signal processing techniques for synthetic aperture acoustic (SAA) and highresolution voxel radar (HRVR) sensing modalities for side-attack explosive ballistic (SAEB) detection are proposed in this thesis. The sensing modalities were vehicle mounted and the data used was collected at an army test site. More specifically, the use of a frequency azimuthal (fraz) feature for SAA and the fusion of a matched filter (MF) and size contrast filter (SCF) for HRVR was explored. For SAA, the focus was to find a signature in the target’s response that would vary as the vehicle’s view on the target changed. For the HRVR, the focus was put on finding objects that were both anomalous (SCF) and target-like (MF). The results in both cases are obtained using receiver operating characteristic (ROC) curves and both are very encouraging.

No description available.

Cyberattacks are becoming a greater concern as our society is digitized to a greater extent, with the storage of sensitive information being a rule rather than an exception. This poses a need of a time- and cost efficient way to assess the cyber security of an enterprise. The threat modeling language enterpriseLang constitute just that, where a general enterprise system assumption allows for re-usage on several enterprise systems. The language is created with Meta Attack Language and is based on the knowledgeable attack- and mitigation steps of MITRE ATT&CK Enterprise Matrix. Since all possible attack paths are not equally likely, probability distributions need to be applied to the attack and mitigation steps. The work presented in this paper includes the provision of probability distributions to a handful of them, mainly connected to gaining initial access to a system with the help of user execution. Beyond this, the financial impact an attack can have and if mitigation measures are financially profitable are examined. To calculate this, a Return on Response Investment model is developed. / Cyberattacker håller på att bli ett större orosmoment allteftersom vårt samhälle digitaliseras i större utsräckning, där lagring av känslig information snarare har blivit regel än undantag. Detta utgör ett behov av ett tids- och kostnadseffektivt sätt att bedömma cybersäkerheten hos ett företag. Hotmodelleringsspråket enterpriseLang är just detta, där antagandet av ett generellt företagssystem möjliggör återanvändning på flera olika system. Språket är skapat med Meta Attack Language och är baserat på kända attack- och försvarssteg från MITRE ATT&CK Enterprise matris. Eftersom alla möjliga attackvägar inte utnyttjas i lika stor utsträckning, behöver sannolikhetsfördelningar tilldelas till attack- och försvarsstegen. Arbetet som presenteras i den här rapporten inkluderar tilldelningen av sannolikhetsfördelningar till en handfull av dem, i synnerhet de kopplade till att få inital åtkomst till ett system med hjälp av användarutföranden. Utöver detta undersöks också den finansiella påverkan en attack kan ha samt om försvarsåtgärder är finansiellt lönsamma. En modell för avkastning på en sådan investering utvecklas för att kunna beräkna detta.

High temperature hydrogen attack (HTHA) is a damage mechanism that only affects carbon steel and low alloy material. Most of the data regarding HTHA are experimental-driven. Even though this approach has been successful, there are still much more things that the oil and gas industry does not understand about HTHA. The regions that were considered safe (below the Nelson curves) have experienced catastrophic failure. Our research consists of performing Molecular Dynamics (MD) and the Nudge Elastic Band (NEB) calculation of HTHA to better understand the atomistic behavior of this damage mechanism.