A Landscape of Adversarial Threats to Machine Learning-Based Intrusion Detection

RÉSUMÉ: L’intelligence artificielle (AI) devient de plus en plus omniprésente dans divers domaines, y compris des secteurs critiques tels que la détection d’intrusions. Les techniques d’apprentissage automatique (ML) sont à l’avant-garde de cette intégration, améliorant la capacité des systèmes de détection d’intrusions (IDS) à détecter et à répondre aux menaces. Ces dernières années ont vu un grand intérêt pour l’adoption de méthodes avancées d’apprentissage profond (DL), offrant une précision et une adaptabilité sans précédent. Cependant, bien que ces méthodes sophistiquées améliorent les performances, elles introduisent également des problèmes de sécurité. Cette thèse explore la sécurité des récentes techniques de ML et de DL dans le contexte de la détection d’intrusions réseau, en particulier leur vulnérabilité aux attaques adverses, ainsi que les contre-mesures qui renforcent leur robustesse. Nos résultats fournissent aux chercheurs et aux praticiens des lignes directrices pour les évaluations de sécurité et des perspectives sur les stratégies de défense. L’apprentissage fédéré (FL) permet à plusieurs entités d’entraînement collaborativement un modèle de ML sans partager de données d’entraînement confidentielles, mais des participants malveillants pourraient perturber l’entraînement du modèle. La première contribution aborde la menace des attaques par empoisonnement sur les modèles de détection d’intrusions basés sur le FL. Nous évaluons l’impact de quatre paramètres d’attaque sur l’efficacité, la furtivité, la cohérence et le moment des attaques par porte dérobée. Nos résultats montrent la détermination de chaque paramètre pour le succès de l’attaque, à condition qu’ils soient ajustés. L’apprentissage par renforcement profond (DRL) est de plus en plus utilisé dans la détection d’intrusions pour son adaptabilité dans des environnements complexes tels que les réseaux informatiques, mais sa dépendance au DL le rend vulnérable aux exemples adverses. La deuxième contribution évalue l’influence des hyperparamètres cruciaux du DRL sur les performances et la robustesse des agents de détection d’intrusions. Incluant des attaques en boîte blanche et en boîte noire à travers la propriété de transférabilité. ABSTRACT: Artificial Intelligence (AI) is becoming increasingly pervasive in various domains, including critical areas such as intrusion detection. Machine Learning (ML) techniques are at the forefront of this integration, enhancing the capability of Intrusion Detection Systems (IDSs) to detect and respond to threats. Recently, advanced Deep Learning (DL) methods have been extensively leveraged, offering unprecedented accuracy and adaptability. However, while these sophisticated methods improve performance, they also introduce security issues. This thesis explores the security of recent ML and DL techniques in the context of network intrusion detection; specifically, their vulnerability to adversarial attacks, and the countermeasures that enhance their robustness. Our findings provide researchers and practitioners with guidelines for security evaluations and insights into defense strategies. Federated Learning (FL) allows multiple entities to train an ML model collaboratively without sharing privacy-sensitive training data. However, malicious participants could interfere with the model training. The first contribution addresses the threat of poisoning attacks on FL-based intrusion detection models. We evaluate the impact of four attack parameters on the effectiveness, stealthiness, consistency, and timing of backdoor attacks. With careful adjustment, our results demonstrate the decisiveness of each parameter for attack success. Deep Reinforcement Learning (DRL) is increasingly employed in intrusion detection for its adaptability in complex environments such as computer networks, but its reliance on DL makes it vulnerable to adversarial examples. The second contribution assesses the influence of crucial DRL hyperparameters on the performance and robustness of intrusion detection agents, including white-box and black-box attacks through the transferability property. While adversarial examples successfully evade ML-based IDSs, they only represent a concrete threat if they can be implemented in real networks. The third contribution investigates the practicality of those adversarial evasion attacks. We study the impact of state-of-the-art attacks on the model performance, data structure, perturbed features, and common successful attacks. We introduce and discuss four crucial criteria for the validity of adversarial examples

Control of 3x7 matrix converter with PWM three intervals modulation

Direct Power conversion from fixed AC voltage into variable AC voltage is gaining a significant attention, especially in case of multi-phases machines/generators; for such reason a new algorithm to control 3x7 matrix converter (MC) is developed in this paper, wherein the main aim is to control multi-phases induction motor/generator connected to the electrical grid with a novel converter (except matrix converter), for that the PWM three intervals modulation strategy is modified from the control of 3x3 MC to 3x7 MC; which is directly connected to the network through a three phase input in order to overcome the supplying problems, on the other side seven phases have been used as an output to benefit the advantages of the multi-phases machines. This paper intends in the first place to explain the 3x3 MC, then to manipulate the control equation for the purpose of making it suitable for controlling the 3x7 MC effectively, thus a good performance can be clearly seen according to the quality of the output voltage/current under typical R-L load, the shift between phases and the THD evaluation. The obtained simulation results which demonstrate the efficiency of the new control algorithm are presented and discussed

Evaluation de l’efficacité du traitement biologique par boue activée au niveau de la station de Bouira.

Les ressources en eaux sur notre planète diminuent de jour après jour par les différentes formes de pollution, ces perturbations dégradent la qualité de l’eau le rendant impropre à la consommation et la réutilisation. Ces dernière décennies les avancés en matière de stations de traitement des eaux usées contribuent à la protection et la conservation de cette ressource pour la génération future. L’Algérie a adopté une nouvelle politique qui consiste à construire des STEP pour le traitement des eaux usée issus des réseaux urbains et domestiques avant leur diversement. Notre étude a été faitedans le but d’évaluer les performances épuratoires de la station de la région de Bouira (rendements en élimination de la pollution par le biais de la DBO5, la DCO et les MES). Notre protocole expérimentale consiste à suivre l’évolution spatio-temporelles de quelques paramètres qui influence sur le processus de traitement par boues actives (pH, température, la charge organique) par une série de données d’analyse des eaux (brutes et traitées). Nos résultats présentent des rendements épuratoires satisfaisants de l’ordre de 93.14 % en termes de MES. Le taux d'abattement de la demande chimique en oxygène (DCO) et la demande biologique en oxygène pendant 5 jours (DBO5) sont respectivement de 82.88 % et de 90.92 %. L’élimination des éléments susceptible d’impliqué un disfonctionnement environnemental (taux de nitrate, de nitrite et d’ortho phosphate) est dans les normes requise

Continuous Nonlinear Model Predictive Current Control of PWM AC/DC Rectifier

The present work applies a nonlinear model predictive current control (NLMPCC) approach to ac/dc pulse width modulation (PWM) rectifier. A cascade structure is used to regulate Dc-link voltage and grid currents. The outer loop objective is to regulate the Dc-link voltage to the desired value, providing the level of the required active power to be used with the reactive power to calculate the referencing current for the inner loop. In the inner loop, the proposed approach is considered. After that, the nonlinear model of the converter is developed, based on continuous minimization of predicted tracking errors, the voltage at the terminal of the converter is deduced. After that, a PWM block is used to generate gate signals. Simulation results are performed to illustrate the efficiency of the proposed control la

Voltage sensorless based virtual flux control of three level NPC back-to-back converter dfigunder grid fault

In this paper, a harmonic elimination of grid and stator currents of doubly fed induction generator (DFIG) in case of grid fault without line voltage sensors is proposed . This can be achieved by compensating power based on virtual flux voltage sensorless technique. Direct power control with space vector modulation (DPC-SVM) is used to control both grid-side (GSC)and rotor-side converters (RSC). To achieve the control objective, compensated active and reactive powers are calculated based on virtual flux technique with balanced and harmonic free current as a control target. A theoretical analysis of active and reactive powers under unbalanced voltage source is clearly demonstrated and the effect of grid fault on the performance of DFIG is profoundly discussed. Simulation results verified the effectiveness of the modified control strategy

Output current observation and control of grid-connected modular multilevel converter using a simplified super twisting algorithm sliding mode

This paper presents a method for observing and controlling the output current of a grid-connected Modular Multilevel Converter (MMC) using a Simplified Super Twisting Algorithm-Sliding Mode (SSTA-SM). This strategy effectively observes and controls the output current under steady-state and dynamic conditions. For the current observation, a Simplified Super Twisting Algorithm-Sliding Mode Luenberger Observer (SSTA-SMLO) is employed. This observer combines the simplicity of the Luenberger Observer (LO) with the robustness of the SSTA-SMO, providing high estimation accuracy while reducing system complexity. Moreover, the proposed Simplified Super Twisting Algorithm-Sliding Mode Control (SSTA-SMC) strategy ensures excellent control performance while maintaining the important features of traditional Sliding Mode Control (SMC). A MATLAB-based simulation and comparative analysis were conducted, evaluating SSTA-SMLO and SSTA-SMC against a Sliding Mode Observer (SMO) and a conventional proportional-integral (PI) controller. The results demonstrate that SSTA-SMC and SSTA-SMLO surpass the classic methods

Study of a Solar PV-Wind-Battery Hybrid Power System for a Remotely Located Region in the Southern Algerian Sahara: Case of Refrigeration

The present work shows an experimental investigation that uses a combination of solar and wind energy as hybrid system (HPS) for electrical generation under the Algerian Sahara area. The generated electricity has been utilized mainly for cooling and freezing. The system has also integrated a gasoline generator to be more reliable. This system is not linked with conventional energy and is not fixed in one region as it is the case of the military base in the Algerian borders. The cooling load consisted of three containers of 10 m3 each with total electricity consumption of 45 kWh/day, two positive rooms (with an internal temperature of +2°C and an external temperature of 35°C) and one negative room (with an internal temperature of -20°C and an external temperature of 35°C). Measurements included the solar radiation intensity, the ambient temperature and the wind speed was collected from Adrar weather station (a windy place in Algeria) for the year of 2010. To simulate the hybrid power system (HPS) HOMER was used. Emissions and renewable energy generation fraction (RF) of total energy consumption are calculated as the main environmental indicator. The net present cost (NPC) and cost of energy (COE) are calculated for economic evaluation. It is found that, for Adrar climates, the optimum results of HPS show a 50% reduction of emissions with 47% of renewable energy fraction

Diffusion-based Adversarial Purification for Intrusion Detection

The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples presents a significant challenge. These crafted perturbations mislead ML models, enabling attackers to evade detection or trigger false alerts. As a reaction, adversarial purification has emerged as a compelling solution, particularly with diffusion models showing promising results. However, their purification potential remains unexplored in the context of intrusion detection. This paper demonstrates the effectiveness of diffusion models in purifying adversarial examples in network intrusion detection. Through a comprehensive analysis of the diffusion parameters, we identify optimal configurations maximizing adversarial robustness with minimal impact on normal performance. Importantly, this study reveals insights into the relationship between diffusion noise and diffusion steps, representing a novel contribution to the field. Our experiments are carried out on two datasets and against 5 adversarial attacks. The implementation code is publicly available

Undiagnosed apical hypertrophic cardiomyopathy in an old amateur soccer player: a case report

Hypertrophic cardiomyopathy is a primary muscle disorder characterized by an abnormal thickness of the left ventricular wall. It is often going undiagnosed because many patients have few symptoms and can lead normal lives. This is a case report about an apical cardiomyopathy diagnosed at a very late stage in an old amateur soccer player. He was hospitalized due to acute chest pain; neurologic disorder related to a hypertensive emergency, he underwent successful percutaneous coronary intervention, echocardiography and CMR revealed Apical hypertrophic cardiomyopathy. The development of sports cardiology has major importance in the detection of cardiac disease which may have poor prognosis. Our patient had the chance to achieve his entire career without rhythmic complications