Literatura científica selecionada sobre o tema "Modèle malveillant"
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Artigos de revistas sobre o assunto "Modèle malveillant"
MPIA TAMFUTU, Roland. "Likelemba et Moziki. Modèles de solidarité participative et laborieuse dans l’esprit d’ubuntu". Cahiers des Religions Africaines 2, n.º 4 (20 de dezembro de 2021): 159–70. http://dx.doi.org/10.61496/ydcy5501.
Texto completo da fonte"Radicalisation des mouvements animalistes : « Enjeux et perspectives pour les intérêts économiques français »". Sécurité et stratégie 31, n.º 3 (19 de março de 2024): 60–66. http://dx.doi.org/10.3917/sestr.031.0060.
Texto completo da fonteFougeyrollas, Patrick. "Handicap". Anthropen, 2016. http://dx.doi.org/10.17184/eac.anthropen.013.
Texto completo da fonteTeses / dissertações sobre o assunto "Modèle malveillant"
Ta, Thanh Dinh. "Modèle de protection contre les codes malveillants dans un environnement distribué". Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0040/document.
Texto completo da fonteThe thesis consists in two principal parts: the first one discusses the message for- mat extraction and the second one discusses the behavioral obfuscation of malwares and the detection. In the first part, we study the problem of “binary code coverage” and “input message format extraction”. For the first problem, we propose a new technique based on “smart” dynamic tainting analysis and reverse execution. For the second one, we propose a new method using an idea of classifying input message values by the corresponding execution traces received by executing the program with these input values. In the second part, we propose an abstract model for system calls interactions between malwares and the operating system at a host. We show that, in many cases, the behaviors of a malicious program can imitate ones of a benign program, and in these cases a behavioral detector cannot distinguish between the two programs
Mansouri, Mohamad. "Performance and Verifiability of IoT Security Protocols". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS065.
Texto completo da fonteThe Internet of Things (IoT) is one of the most important technologies in our current world. It is composed of connected devices with sensors and processing abilities, all connected to a single platform that orchestrates them. The integration of these IoT devices into many real-life applications (eg., transportation, industries, ...) implies significant performance and efficiency improvements. As a consequence, we have seen a boom in the number of IoT devices deployed and their corresponding platforms. These IoT devices use real-time data from their deployment environment and send them to the platform. The collected data by these devices often consist of sensitive information belonging to the individual who uses this technology. Hence, the privacy of users' data is one of the important concerns in IoT. Moreover, IoT applications rely on automating frequent tasks to achieve better efficiency. Unfortunately, moving control of usually human-controlled operations to the IoT presents some non-negligible risks to the safety of IoT users. This thesis deals with the privacy and safety concerns raised by IoT. We propose security protocols that preserve the privacy of the users' data. In addition to privacy, we design verifiable solutions that guarantee the correctness of the computations performed by the IoT devices and the platform and hence increase trust toward this technology. We design these solutions while focusing on their performance. More precisely, we propose protocols that are scalable to cope with the increasing number of IoT devices. We also consider protocols that are fault-tolerant to cope with the frequent dropouts of IoT devices. We particularly focus on two security protocols: Secure Aggregation and Remote Attestation. Secure aggregation is a protocol where an aggregator computes the sum of the private inputs of a set of users. In this thesis, we propose the first verifiable secure aggregation protocol (VSA) that gives formal guarantees of security in the malicious model. Our solution preserves the privacy of users' inputs and the correctness of the aggregation result. Moreover, we propose a novel fault-tolerant secure aggregation protocol (FTSA) based on additively-homomorphic encryption. The scheme allows users in secure aggregation to drop from the protocol and offers a mechanism to recover the aggregate without affecting the privacy of the data. We show that FTSA outperforms the state-of-the-art solutions in terms of scalability with respect to the number of users. On the other hand, a remote attestation protocol is a protocol that allows an IoT device (acting as a prover) to prove its software integrity to the IoT platform (acting as the verifier). We propose a new collaborative remote attestation protocol (FADIA) in which devices collect attestations from each other and aggregate them. FADIA deals with the heterogeneity and dynamic nature of IoT by considering fairness in its design. The evaluation of FADIA shows an increase in the lifetime of the overall network
Merino, Laso Pedro. "Détection de dysfonctionements et d'actes malveillants basée sur des modèles de qualité de données multi-capteurs". Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0056/document.
Texto completo da fonteNaval systems represent a strategic infrastructure for international commerce and military activity. Their protection is thus an issue of major importance. Naval systems are increasingly computerized in order to perform an optimal and secure navigation. To attain this objective, on board vessel sensor systems provide navigation information to be monitored and controlled from distant computers. Because of their importance and computerization, naval systems have become a target for hackers. Maritime vessels also work in a harsh and uncertain operational environments that produce failures. Navigation decision-making based on wrongly understood anomalies can be potentially catastrophic.Due to the particular characteristics of naval systems, the existing detection methodologies can't be applied. We propose quality evaluation and analysis as an alternative. The novelty of quality applications on cyber-physical systems shows the need for a general methodology, which is conceived and examined in this dissertation, to evaluate the quality of generated data streams. Identified quality elements allow introducing an original approach to detect malicious acts and failures. It consists of two processing stages: first an evaluation of quality; followed by the determination of agreement limits, compliant with normal states to identify and categorize anomalies. The study cases of 13 scenarios for a simulator training platform of fuel tanks and 11 scenarios for two aerial drones illustrate the interest and relevance of the obtained results
Legrand, Judith. "La modélisation mathématique dans le cadre de la préparation contre une épidémie d'origine malveillante : application à la fièvre hémorragique Ebola et à la variole". Paris 6, 2006. http://www.theses.fr/2006PA066196.
Texto completo da fonteEl, Hatib Souad. "Une approche sémantique de détection de maliciel Android basée sur la vérification de modèles et l'apprentissage automatique". Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/66322.
Texto completo da fonteThe ever-increasing number of Android malware is accompanied by a deep concern about security issues in the mobile ecosystem. Unquestionably, Android malware detection has received much attention in the research community and therefore it becomes a crucial aspect of software security. Actually, malware proliferation goes hand in hand with the sophistication and complexity of malware. To illustrate, more elaborated malware like polymorphic and metamorphic malware, make use of code obfuscation techniques to build new variants that preserve the semantics of the original code but modify it’s syntax and thus escape the usual detection methods. In the present work, we propose a model-checking based approach that combines static analysis and machine learning. Mainly, from a given Android application we extract an abstract model expressed in terms of LNT, a process algebra language. Afterwards, security related Android behaviours specified by temporal logic formulas are checked against this model, the satisfaction of a specific formula is considered as a feature, finally machine learning algorithms are used to classify the application as malicious or not.
Nguyen, Huu vu. "On CARET model-checking of pushdown systems : application to malware detection". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC061/document.
Texto completo da fonteThe number of malware is growing significantly fast. Traditional malware detectors based on signature matching or code emulation are easy to get around. To overcome this problem, model-checking emerges as a technique that has been extensively applied for malware detection recently. Pushdown systems were proposed as a natural model for programs, since they allow to keep track of the stack, while extensions of LTL and CTL were considered for malicious behavior specification. However, LTL and CTL like formulas don't allow to express behaviors with matching calls and returns. In this thesis, we propose to use CARET (a temporal logic of calls and returns) for malicious behavior specification. CARET model checking for Pushdown Systems (PDSs) was never considered in the literature. Previous works only dealt with the model checking problem for Recursive State Machine (RSMs). While RSMs are a good formalism to model sequential programs written in structured programming languages like C or Java, they become non suitable for modeling binary or assembly programs, since, in these programs, explicit push and pop of the stack can occur. Thus, it is very important to have a CARET model checking algorithm for PDSs. We tackle this problem in this thesis. We reduce it to the emptiness problem of Büchi Pushdown Systems. Since CARET formulas for malicious behaviors are huge, we propose to extend CARET with variables, quantifiers and predicates over the stack. This allows to write compact formulas for malicious behaviors. Our new logic is called Stack linear temporal Predicate logic of CAlls and RETurns (SPCARET). We reduce the malware detection problem to the model checking problem of PDSs against SPCARET formulas, and we propose efficient algorithms to model check SPCARET formulas for PDSs. We implemented our algorithms in a tool for malware detection. We obtained encouraging results. We then define the Branching temporal logic of CAlls and RETurns (BCARET) that allows to write branching temporal formulas while taking into account the matching between calls and returns and we proposed model-checking algorithms of PDSs for BCARET formulas. Finally, we consider Dynamic Pushdown Networks (DPNs) as a natural model for multithreaded programs with (recursive) procedure calls and thread creation. We show that the model-checking problem of DPNs against CARET formulas is decidable
Darwaish, Asim. "Adversary-aware machine learning models for malware detection systems". Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7283.
Texto completo da fonteThe exhilarating proliferation of smartphones and their indispensability to human life is inevitable. The exponential growth is also triggering widespread malware and stumbling the prosperous mobile ecosystem. Among all handheld devices, Android is the most targeted hive for malware authors due to its popularity, open-source availability, and intrinsic infirmity to access internal resources. Machine learning-based approaches have been successfully deployed to combat evolving and polymorphic malware campaigns. As the classifier becomes popular and widely adopted, the incentive to evade the classifier also increases. Researchers and adversaries are in a never-ending race to strengthen and evade the android malware detection system. To combat malware campaigns and counter adversarial attacks, we propose a robust image-based android malware detection system that has proven its robustness against various adversarial attacks. The proposed platform first constructs the android malware detection system by intelligently transforming the Android Application Packaging (APK) file into a lightweight RGB image and training a convolutional neural network (CNN) for malware detection and family classification. Our novel transformation method generates evident patterns for benign and malware APKs in color images, making the classification easier. The detection system yielded an excellent accuracy of 99.37% with a False Negative Rate (FNR) of 0.8% and a False Positive Rate (FPR) of 0.39% for legacy and new malware variants. In the second phase, we evaluate the robustness of our secured image-based android malware detection system. To validate its hardness and effectiveness against evasion, we have crafted three novel adversarial attack models. Our thorough evaluation reveals that state-of-the-art learning-based malware detection systems are easy to evade, with more than a 50% evasion rate. However, our proposed system builds a secure mechanism against adversarial perturbations using its intrinsic continuous space obtained after the intelligent transformation of Dex and Manifest files which makes the detection system strenuous to bypass
Wang, Tairan. "Decision making and modelling uncertainty for the multi-criteria analysis of complex energy systems". Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0036/document.
Texto completo da fonteThis Ph. D. work addresses the vulnerability analysis of safety-critical systems (e.g., nuclear power plants) within a framework that combines the disciplines of risk analysis and multi-criteria decision-making. The scientific contribution follows four directions: (i) a quantitative hierarchical model is developed to characterize the susceptibility of safety-critical systems to multiple types of hazard, within the needed `all-hazard' view of the problem currently emerging in the risk analysis field; (ii) the quantitative assessment of vulnerability is tackled by an empirical classification framework: to this aim, a model, relying on the Majority Rule Sorting (MR-Sort) Method, typically used in the decision analysis field, is built on the basis of a (limited-size) set of data representing (a priori-known) vulnerability classification examples; (iii) three different approaches (namely, a model-retrieval-based method, the Bootstrap method and the leave-one-out cross-validation technique) are developed and applied to provide a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments), accounting for the uncertainty introduced into the analysis by the empirical construction of the vulnerability model; (iv) on the basis of the models developed, an inverse classification problem is solved to identify a set of protective actions which effectively reduce the level of vulnerability of the critical system under consideration. Two approaches are developed to this aim: the former is based on a novel sensitivity indicator, the latter on optimization.Applications on fictitious and real case studies in the nuclear power plant risk field demonstrate the effectiveness of the proposed methodology