Avec la mondialisation du marché des semi-conducteurs, l'intégrité des circuits intégrés (CI) est devenue préoccupante... On distingue deux menaces principales : les chevaux de Troie matériel (CTM) et les contrefaçons. La principale limite des méthodes de vérification de l’intégrité proposées jusqu'à maintenant est le biais induit par les variations des procédés de fabrication. Cette thèse a pour but de proposer une méthode de détection embarquée de détection de CTM et de contrefaçons. À cette fin, une caractérisation de l'impact des modifications malveillantes sur un réseau de capteurs embarqué a été effectuée. L'addition malicieuse de portes logiques (CTM) ou la modification de l'implémentation du circuit (contrefaçons) modifie la distribution de la tension à la l'intérieur du circuit. Une nouvelle approche est proposée afin d'éliminer l'influence des variations des procédés. Nous posons que pour des raisons de cout et de faisabilité, une infection est faite à l'échelle d'un lot de production. Un nouveau modèle de variation de performance temporelle des structures CMOS en condition de design réel est introduit. Ce modèle est utilisé pour créer des signatures de lots indépendantes des variations de procédé et utilisé pour définir une méthode permettant de détecter les CTMs et les contrefaçons.Enfin nous proposons un nouveau distingueur permettant de déterminer, avec un taux de succès de 100%, si un CI est infecté ou non. Ce distingueur permet de placer automatiquement un seuil de décision adapté à la qualité des mesures et aux variations de procédés. Les résultats ont été expérimentalement validés sur un lot de cartes de prototypage FPGA. / Due to the trend to outsourcing semiconductor manufacturing, the integrity of integrated circuits (ICs) became a hot topic. The two mains threats are hardware Trojan (HT) and counterfeits. The main limit of the integrity verification techniques proposed so far is that the bias, induced by the process variations, restricts their efficiency and practicality. In this thesis we aim to detect HTs and counterfeits in a fully embedded way. To that end we first characterize the impact of malicious insertions on a network of sensors. The measurements are done using a network of Ring oscillators. The malicious adding of logic gates (Hardware Trojan) or the modification of the implementation of a different design (counterfeits) will modify the voltage distribution within the IC.Based on these results we present an on-chip detection method for verifying the integrity of ICs. We propose a novel approach which in practice eliminates this limit of process variation bias by making the assumption that IC infection is done at a lot level. We introduce a new variation model for the performance of CMOS structures. This model is used to create signatures of lots which are independent of the process variations. A new distinguisher has been proposed to evaluate whether an IC is infected. This distinguisher allows automatically setting a decision making threshold that is adapted to the measurement quality and the process variation. The goal of this distinguisher is to reach a 100\% success rate within the set of covered HTs family. All the results have been experientially validated and characterized on a set of FPGA prototyping boards.

abstract: The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation. In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. It is becoming increasingly necessary to trust but verify the received devices both at production and in the field. Unauthorized hardware or firmware modifications, known as Trojans, can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices. Since Trojans may be triggered in the field at an unknown instance, it is essential to detect their presence at run-time. However, it isn't easy to run sophisticated detection algorithms on these devices due to limited computational power and energy, and in some cases, lack of accessibility. Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain. In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory. When the device enters the test mode, a predefined test application is run on the device repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes. The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

The most important requirement for running a nuclear power plant safely is having a reliable safety system, especially during the emergency shutdown condition. For performing a scrutiny load flow and voltage drop study a detailed and comprehensive electrical model for the emergency electrical distribution system of the Trojan Nuclear Power Plant has been developed. This model includes the representation of the transformers, circuit breakers, motors, cables and load data from 4160 volts level down to the individual 480 volts loads.

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I uppsatsen har Anom och Operation Trojan Shield analyserats utifrån frågeställning­arna hur domstolarna har hanterat bevisningen som tillkommit genom insatsen, vilka bevisförbud som finns att tillgå i svensk rätt, om de bör tillämpas under förevarande omständigheter samt slutligen om några förändringar av bevisreglerna är påkallade med hänsyn till vad som framkommit i uppsatsen i övrigt. Operation Trojan Shield var en insats som skulle hjälpa de brottsbekämpande myndigheterna att lagföra företrädare för den grova organiserade brottsligheten. Anom var emellertid inte den första krypterade kommunikationstjänsten som domstolarna fick på sitt bord. Föregångaren EncroChat hade redan hanterats av underrätterna. Det föreligger dock väsentliga skillnader i de olika kommunikationstjänsterna uppkomstsätt, vilket borde fått genomslag i domstolarnas hantering. Insatsen Operation Trojan Shield har karaktär av brottsprovokation, eftersom Anom-enheterna började användas i samband med att andra krypterade kommunikationstjänster försvann från marknaden. Amerikanska brottsbekämpande myndigheter har upprättat och marknadsfört tjänsten på ett sätt som väckt en brottslig vilja, åtminstone hos högnivådistributörerna inom nätverksbrottsligheten. Genom att svenska domstolar sedan har tillämpat samma bevisrättsliga regler på Anom-material som EncroChat-material har resultatet varit otillfredsställande i relation till rätten till rättvis rättegång.

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<p>With the fast spread of machine learning techniques, sharing and adopting public deep neural networks become very popular. As deep neural networks are not intuitive for human to understand, malicious behaviors can be injected into deep neural networks undetected. We call it trojan attack or backdoor attack on neural networks. Trojaned models operate normally when regular inputs are provided, and misclassify to a specific output label when the input is stamped with some special pattern called trojan trigger. Deploying trojaned models can cause various severe consequences including endangering human lives (in applications like autonomous driving). Trojan attacks on deep neural networks introduce two challenges. From the attacker's perspective, since the training data or training process is usually not accessible to the attacker, the attacker needs to find a way to carry out the trojan attack without access to training data. From the user's perspective, the user needs to quickly scan the online public deep neural networks and detect trojaned models.</p> <p>We try to address these challenges in this dissertation. For trojan attack without access to training data, We propose to invert the neural network to generate a general trojan trigger, and then retrain the model with reverse-engineered training data to inject malicious behaviors to the model. The malicious behaviors are only activated by inputs stamped with the trojan trigger. To scan and detect trojaned models, we develop a novel technique that analyzes inner neuron behaviors by determining how output activation change when we introduce different levels of stimulation to a neuron. A trojan trigger is then reverse-engineered through an optimization procedure using the stimulation analysis results, to confirm that a neuron is truly compromised. Furthermore, for complex trojan attacks, we propose a novel complex trigger detection method. It leverages a novel symmetric feature differencing method to distinguish features of injected complex triggers from natural features. For trojan attacks on NLP models, we propose a novel backdoor scanning technique. It transforms a subject model to an equivalent but differentiable form. It then inverts a distribution of words denoting their likelihood in the trigger and applies a novel word discriminativity analysis to determine if the subject model is particularly discriminative for the presence of likely trigger words.</p>

La multiplication des résistances aux antibiothérapies actuelles et l’utilisation potentielle de bactéries pathogènes dans le cadre d’attentats bioterroristes rendent nécessaire la recherche de nouvelles cibles biologiques et la découverte de nouvelles stratégies antibiotiques. Dans ce contexte, les mécanismes d’assimilation du fer chez les bactéries à Gram négatif sont des cibles particulièrement prometteuses. Le fer est en effet un élément essentiel à la vie, mais peu biodisponible. Les bactéries ont donc développé des mécanismes efficaces pour subvenir à leurs besoins en fer. Ces mécanismes de transport nécessitent un apport d’énergie fourni par une machinerie bactérienne complexe, la machinerie TonB. La protéine TonB, qui joue un rôle central dans le fonctionnement de cette machinerie, est la cible de notre approche. Nous souhaitons séquestrer cette protéine dans le périplasme grâce à des composés peptidiques fonctionnalisés par des hétérocycles de type isoindole ou 1,2,4-triazine. La conception et la synthèse de ces molécules sont présentées dans ce manuscrit, ainsi que leurs perspectives de vectorisation en utilisant une stratégie dite du "cheval de Troie". Notre contribution à la mise au point d’un test d’affinité in vitro est également abordée. / The increasing resistances to the current antibiotherapies, and the potential use of pathogenic bacteria as biological weapons led us to the absolute necessity of discovering new biological targets and new antibiotic strategies. In this context, iron uptake pathways of Gram negative bacteria are promising targets. Indeed, iron is an essential nutrient, but it has a low bioavailability. Bacteria have developed efficient iron uptake pathways in order to proliferate. Iron is transported in the bacterial cell by specific outer membrane transporters and thanks to the energy provided by a complex molecular machinery, called TonB. The TonB protein, which is the keystone of this machinery, is a key target for the development of new antibiotics. We would like to sequester this protein in the periplasm thanks to molecules constituted of a peptidic moiety and a heterocyclic moiety such as isoindole or 1,2,4-triazine. The conception and the synthesis of these compounds are presented in this document, as well as their possibilities to be vectorized using a “Trojan Horse” strategy. Our contribution to the development of an in vitro test of affinity is presented as well.

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<p>Advances in Artificial Intelligence (AI), or more precisely on Neural Networks (NNs), and fast processing technologies (e.g. Graphic Processing Units or GPUs) in recent years have positioned NNs as one of the main machine learning algorithms used to solved a diversity of problems in both academia and the industry. While they have been proved to be effective in solving many tasks, the lack of security guarantees and understanding of their internal processing disrupts their wide adoption in general and cybersecurity-related applications. In this dissertation, we present the findings of a comprehensive study aimed to enable the absorption of state-of-the-art NN algorithms in the development of enterprise solutions. Specifically, this dissertation focuses on (1) the development of defensive mechanisms to protect NNs against adversarial attacks and (2) application of NN models for anomaly detection in enterprise networks.</p><p>In this state of affairs, this work makes the following contributions. First, we performed a thorough study of the different adversarial attacks against NNs. We concentrate on the attacks referred to as trojan attacks and introduce a novel model hardening method that removes any trojan (i.e. misbehavior) inserted to the NN models at training time. We carefully evaluate our method and establish the correct metrics to test the efficiency of defensive methods against these types of attacks: (1) accuracy with benign data, (2) attack success rate, and (3) accuracy with adversarial data. Prior work evaluates their solutions using the first two metrics only, which do not suffice to guarantee robustness against untargeted attacks. Our method is compared with the state-of-the-art. The obtained results show our method outperforms it. Second, we proposed a novel approach to detect anomalies using LSTM-based models. Our method analyzes at runtime the event sequences generated by the Endpoint Detection and Response (EDR) system of a renowned security company running and efficiently detects uncommon patterns. The new detecting method is compared with the EDR system. The results show that our method achieves a higher detection rate. Finally, we present a Moving Target Defense technique that smartly reacts upon the detection of anomalies so as to also mitigate the detected attacks. The technique efficiently replaces the entire stack of virtual nodes, making ongoing attacks in the system ineffective.</p><p> </p>