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OASIS: An Intrusion Detection System Embedded in Bluetooth Low Energy Controllers
No full textInternational audienceBluetooth Low Energy has established itself as one of the central protocols of the Internet of Things. Its many features (mobility, low energy consumption) make it an attractive protocol for smart devices. However, numerous critical vulnerabilities affecting BLE have been made public in recent years, some of which are linked to the protocol's design itself. The impossibility of correcting these vulnerabilities without affecting the specification requires the development of effective intrusion detection systems, enabling the detection and prevention of these threats. Unfortunately, the protocol relies on peer-to-peer communications and introduces many complex and dynamic mechanisms (e.g., channel hopping), making monitoring complex, costly and limited. Existing intrusion detection approaches lack flexibility, are limited in scope and introduce high deployment costs.In this paper, we explore a novel approach consisting in embedding an intrusion detection system directly within BLE controllers. This strategic position tackles these challenges by enabling a more advanced analysis and instrumentation of the protocol and opens the way to new defensive applications. We propose OASIS, a framework for injecting detection heuristics into controllers' firmwares in a generic way without affecting the normal operation of theprotocol stack. It can be deployed in various contexts during the life cycle of a device, from the chip manufacturer to a software developer making use of proprietary components, or even in a full black box context by a security analyst to harden a commercial product. We describe its modular architecture and present its implementation within five of the most popular BLE chips from three different manufacturers, deployed in billions of devices and embedding heterogeneous protocol stacks. We present five modules for critical low-level protocol attack detection. We show that OASIS has a low impact on the controller performance (power, timing, memory) and evaluate its usage in a real-world setting
Numerical modelling the reaction propagation in Al based thermites
No full textInternational audienceReactive materials or nanothermites are composites or physical mixtures that demonstrate selfsustaining exothermic reactions upon receiving an initial energy input. This category of materials is distinctive within the realm of energetic materials, differing from explosives in the manner in which reaction fronts propagate subsonically and rely on atomic diffusion or other physical transport mechanisms. A range of studies have explored the numerical modeling of reaction propagation in Albased thermites. Kim [1] and Tichtchenko [2] both focused on the self-propagation of combustion waves in nanoscale thermite composites, with Kim's model predicting wave speed and Tichtchenko's model analyzing the reaction front progression rate. Lahiner [3] proposed a diffusion-reaction scheme for predicting ignition and reaction dynamics in Al/CuO multilayered thin films, considering the decomposition of CuO and the diffusion of released oxygen. Tichtchenko [4] developed a model for predicting gas generation during the reaction of aluminum-based thermites, with a focus on pressure generation and its components. These studies contributed to the understanding of combustion of Albased thermites, but do not consider coupled gas-condensed phased processes. In nano-sized materials especially, the combustion processes include thermal conduction, phase change, mass diffusion etc., which happen simultaneously and play an important role [5]. However, models considering coupling of thermo-mechanical-chemical physics are still absent. In this talk, I will present a novel one-dimensional deflagration model that describes the dynamics of the reaction front propagation in Al/CuO powdered thermite considering the reacting flow combined with heat transfer, chemistry and fluid flow. Separate mass, momentum and energy transport equations for the three phases, namely Al, CuO particles and gas mixture, are written in the frame of N-Euler approach for multiphase reactive flows. These equations are coupled by modeled interphase transfer terms
Impact of source modeling and poroelastic models on numerical modeling of unconsolidated granular media: application at the laboratory scale
No full textInternational audienceThe near surface is characterized by using different numerical techniques, among them seismic techniques which are non-destructive. More particularly, for a better understanding of acoustic and seismic measurements in unconsolidated granular media that can constitute the near surface, many studies have been conducted in situ and also at the laboratory scale where theoretical models have been developed. In this article, we want to model such granular media that are difficult to characterize. At the laboratory scale, dry granular media can be modeled with a homogenized power-law elastic model that depends on depth. In this context, we validate numerically a similar power-law elastic model for such media by applying it to a homogenized elastic medium or to the solid frame of a poroelastic medium that consists of solid and air components. By comparing the response of both rheologies, we want to highlight what poroelastic media can bring to better reproduce the experimental data in the time and frequency domains. To achieve this objective, we revisit studies carried out on unconsolidated granular media at the laboratory scale and we compare different models with different rheologies (elastic or poro-elastic), dimensions (2D or 3D), boundary conditions (PML or Dirichlet) and locations of the source (modelled as a vibratory stick or a point force) in order to reproduce the experimental data. We show here that a poroelastic model describes better the amplitudes of the seismograms. Furthermore, we study the sensitivity of the seismic data to the source location which is crucial to improve the amplitude of the signals and the detection of the different seismic modes
Accurate Anomaly Detection Leveraging Knowledge-enhanced GAT
No full textInternational audienceAnomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection
Reduced-cost EnKF for parameter estimation of microscale atmospheric pollutant dispersion models
No full textInternational audienc
The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity
No full textInternational audienceOne of the many unresolved obstacles in the field of cardiovascular research is an uncompromising in vitro cardiac model. While primary cell sources from animal models offer both advantages and disadvantages, efforts over the past half-century have aimed to reduce their use. Additionally, obtaining a sufficient quantity of human primary cardiomyocytes faces ethical and legal challenges. As the practically unlimited source of human cardiomyocytes from induced pluripotent stem cells (hiPSC-CM) is now mostly resolved, there are great efforts to improve their quality and applicability by overcoming their intrinsic limitations. The greatest bottleneck in the field is the in vitro ageing of hiPSC-CMs to reach a maturity status that closely resembles that of the adult heart, thereby allowing for more appropriate drug developmental procedures as there is a clear correlation between ageing and developing cardiovascular diseases. Here, we review the current state-of-the-art techniques in the most realistic heart models used in disease modelling and toxicity evaluations from hiPSC-CM maturation through heart-on-a-chip platforms and in silico models to the in vitro models of certain cardiovascular diseases
Long-Range Reading of Multiple Chipless Sensors from the Isoline Processing of 3D Radar Images
No full textInternational audienceIn this paper, we report the long-range and wireless interrogation of multiple chipless sensors from the isoline processing of three-dimensional polarimetric radar images. A Frequency-Modulated Continuous-Wave Radar operating at 24 GHz is used for the indoor interrogation of four sensors in the basement of a Laboratory. In such cluttered environment, the proposed radar image processing based on isolines computation allows the wireless measurement range of sensors up to 5.8m.</div
Low-Voltage Schottky p-GaN HEMT Properties under Extreme Repetitive Short-Circuit Operation Conditions : 2DEG Pinch-off, Stability, Aging, Robustness and Failure-Modes Analysis [Abstract]
No full textInternational audienceThe authors proposed in-depth experimentation and physical analysis showing the extreme robustness capability of low-voltage GaN HEMT in single and repetitive short-circuit. A 2DEG pinch-off behavior is analyzed depending on VDS voltage and charges' trapping / de-trapping relaxation time. A new drain-gate leakage-current mechanism at turn-off is suggested to explain the ultimate thermal-runaway failure-mechanism
Programmation des systèmes embarqués: Application aux μcontrôleurs STM32F10x
No full textNational audienceNombre d’objets de notre quotidien sont devenus embarqués et connectés, voire autonomes. Les ingénieurs et techniciens qui les développent doivent avoir des compétences à la fois en informatique et en électronique.S’appuyant sur une vingtaine d’années d’expérience dans le domaine de l’informatique dite matérielle et embarquée, cet ouvrage analyse comment des objets physiques peuvent interagir avec des microcontrôleurs. Il présente les principes fondamentaux de programmation et de structuration de code. Bien que basés sur une famille particulière (STM32), les différents chapitres exposent les concepts généraux applicables à n’importe quel μcontrôleur. Ils analysent ainsi les mécanismes qui régissent les échanges entre un programme informatique et un élément matériel de l’objet embarqué.Chacun des chapitres traitant de la programmation des unités périphériques se termine par un exemple reprenant une application fil rouge de gestion de chauffage d’un logement équipé d’une installation photovoltaïque pour illustrer une mise en oeuvre en langage C
Apprentissage machine guidé par des connaissances pour le diagnostic
No full textNational audienceModel-based diagnosis requires full knowledge about the analyzed system. On the other hand, data-driven diagnosis lacks explanations about the cause of a fault. Many applications require reliable and explainable fault diagnosis and would benefit from knowing the cause of faults in order to avoid them. In this thesis, the focus is on developing new methods combining model-based and data-driven diagnosis in a synergistic way. Specifically, the emphasis is on structural analysis as a model-based method that requires only knowledge of the system's structure.For that purpose, a novel explainable method has been designed called DT4X (Diagnosis Tree for eXplainability). It leverages decision trees where decisions are informed by diagnosis meta knowledge, specifically focusing on the properties of diagnosis indicators. This knowledge is used at each node to articulate a symbolic classification problem, outputting discriminating functions. The outcome is a multivariate decision tree that produces a compact model for diagnosis. The use of decision trees increases the explainability of the outcome, all the more so as one discovers the explicit formal expressions of diagnosis indicators, structured in the form of analytical redundancy relations.On simple systems, DT4X proves to output expressions that could previously only be found with full physical knowledge of the system. Its accuracy is higher than traditional machine learning algorithms. On more complex dynamic systems, DT4X reaches very high accuracy but lacks interpretable insight about the studied system.On logical circuits, a preprocessing of the data is proposed to remove samples corresponding to masked faults. DT4X finds logical expressions that possess all the properties of model-based diagnosis indicators.A variant of DT4X has been developed called PI-DT4X (Physics Informed DT4X). It is an alternative that requires more physical insight about the system but has higher accuracy and capacity to find relevant diagnosis indicators. PI-DT4X takes as input the structural model of the system and injects specific structural sub-models in the decision tree to guide and focus symbolic regression, so that diagnosis indicators are discovered faster and easier.Le diagnostic basé modèle requiert une connaissance complète du système analysé. Par ailleurs, le diagnostic basé sur les données manque d'explications sur la cause d'une défaillance. De nombreuses applications nécessitent un diagnostic de panne fiable et explicatif, et bénéficieraient de connaître la cause des défaillances afin de les éviter. Dans cette thèse, l'accent est mis sur le développement de nouvelles méthodes combinant le diagnostic basé sur le modèle et le diagnostic basé sur les données de manière synergique. Plus précisément, l'accent est mis sur l'analyse structurelle en tant que méthode basée sur le modèle qui ne nécessite que la connaissance de la structure du système. Dans ce but, une nouvelle méthode explicative a été conçue, appelée DT4X (Diagnosis Tree for eXplainability). Elle exploite les arbres de décision où les critères de décision sont construits à partir de méta-connaissance des méthodes de diagnostic, se concentrant spécifiquement sur les propriétés des indicateurs de diagnostic. Cette connaissance est utilisée à chaque nœud pour articuler un problème de classification symbolique, produisant des fonctions discriminantes. Le résultat est un arbre de décision multivarié qui produit un algorithme de diagnostic. L'utilisation d'arbres de décision augmente l'explicabilité du résultat, d'autant plus que l'on découvre les expressions formelles explicites des indicateurs de diagnostic, structurées sous forme de relations de redondance analytique. Sur des systèmes simples, DT4X s'avère produire des expressions qui ne pouvaient auparavant être trouvées qu'avec une connaissance physique complète du système. Sa précision est supérieure à celle des algorithmes d'apprentissage automatique traditionnels. Sur des systèmes dynamiques plus complexes, DT4X atteint une précision trè! s élevée mais manque d'aperçu interprétable sur le système étudié.Sur les circuits logiques, un prétraitement des données est proposé pour supprimer les échantillons correspondant à des défaillances masquées. DT4X trouve des expressions logiques qui possèdent toutes les propriétés des indicateurs de diagnostic basés modèle. Une variante de DT4X a été développée, appelée PI-DT4X (Physically Informed Diagnosis Tree for eXplainability). Il s'agit d'une alternative qui nécessite une meilleure compréhension physique du système mais présente une précision plus élevée et une meilleure capacité à trouver des indicateurs de diagnostic pertinents. PI-DT4X prend en entrée le modèle structurel du système et injecte des sous-modèles structurels spécifiques dans l'arbre de décision pour guider et concentrer la régression symbolique, de sorte que les indicateurs de diagnostic soient découverts plus rapidement et plus assurément