International Conference Papers

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    Side Channel Attack using Machine Learning
    (IEEE, 2022-12-15) Amrouche, Amina; Boubchir, Larbi; Yahiaoui, Saïd
    The overwhelming majority of significant security threats are hardware-based, where the attackers attempt to steal information straight from the hardware that our secure and encrypted software operates on. Unquestionably, side-channel attacks are one of the most severe risks to hardware security. Rather than depending on bugs in the program itself, a side-channel attack exploits information leaked from the program implementation in order to exfiltrate sensitive secret information such as cryptographic keys. A side channel assault could manifest in different ways including electromagnetic radiation, power consumption, timing data, or even acoustic emanation. Ever since the side-channel attacks were introduced in the 1990s, a number of significant attacks on cryptographic implementations utilizing side-channel analysis have emerged, such as template attacks, and attacks based on power analysis and electromagnetic analysis. However, Artificial Intelligence has become more prevalent. Attackers are now more interested in machine learning and deep learning technologies that enable them to interpret the extracted raw data. The aim of this paper is to highlight the main methods of machine learning and deep learning that are used in the most recent studies that deal with different types of side-channel attacks.
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    UDEPLOY: User-Driven Learning for Occupancy Sensors DEPLOYment In Smart Buildings
    (IEEE, 2018-03) Laidi, Roufaida; Djenouri, Djamel
    A solution for motion sensors deployment in smart buildings is proposed. It differentiates the monitored zones according to their occupancy, where highly-occupied zones have higher coverage requirements over low-occupied zones, and thus are assigned higher granularity in the targeted coverage (weights). The proposed solution is the first that defines a user-driven approach, which uses sampling of occupants’ behavior to determine the zones and the coverage weights. The samples are acquired during a short learning phase and then used to derive a graph model. The latter is plugged into a greedy, yet effective, algorithm that seeks optimal placement for maximizing detection accuracy while reducing the cost (number of sensors). Practical aspects such as the scalability and the applicability of the solution are considered. A simulation study that compares the proposed solution with two state-of-the-art solutions shows the superiority of the proposed approach in the accuracy of detection (increased coverage), and scalability (reduced runtime).
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    Filtrage cognitif de l’information électronique
    (ECIG 2007, 2007-10-19) Nouali, Omar; Toursel, B.
    L'objectif des travaux de recherche présentés dans cet article est l’automatisation du processus de filtrage de l’information en prenant en compte l’importance relative de l'information et les besoins en ressources linguistiques pour son traitement. Nous proposons une solution ouverte, dynamique et évolutive qui offre au processus de filtrage la possibilité d’apprendre, d’exploiter ces connaissances apprises et de s’adapter à la nature de l’application. Nous l’avons modélisé à l’aide d’agents pour offrir un gain de temps par rapport à une solution algorithmique séquentielle. Pour la validation de notre approche de filtrage, nous avons mené un ensemble d’expériences pour évaluer les performances des techniques et outils proposés et développés.