A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET

Programmation linéaire et pénalité mixte : un nouvel algorithme de type intérieur-extérieur

Le but principal de ce travail est de développer une nouvelle classe d'algorithmes de type intérieur-extérieur pour la programmation linéaire. Le principe de ces algorithmes est basé sur la résolution d'une suite de sous-problèmes pénalisés : (formule(s) : voir document). En étudiant le système (S): {ZX e-µe = 0, ATy+z-c = 0, Ax-b+µy = 0, x > 0} qui correspond à la condition nécessaire d'optimalité pour le problème (Pµ,), nous remarquons que la solution (x(µ), y(µ), z(µ)) de (S) tend vers les solutions optimales primale et duale du problème linéaire original. Les principales étapes sont : la définition d'une "pseudo-marge" qui permet d'une part d'évaluer la qualité des solutions primale-duale et d'autre part de caractériser l'optimalité la détermination d'une stratégie pour la mise à jour du paramètre µk de façon à assurer une décroissance linéaire globale de Δ(µ) la mise en oeuvre d'un algorithme de minimisation interne pour approcher x(µk), y(µk) et z(µk) , la définition d'un algorithme qui résume la démarche

Outils d’aide à la décision pour la planification des réseaux de distribution de l’énergie électrique

International audienceThe continued growth in demand for electricity is a increasingly challenge for the company. This requires great efforts to optimize decisions to be taken especially for managing the distribution of electricity which poses many problems in society, primarily due to the expansion of the network, increased consumption Power and real-time management. As the strengthening of electrical networks is difficult and expensive at the same time, it is necessary to choose an optimal management to ensure customer satisfaction, reduce costs and increase profit margins. In this work, we propose a few different optimization methods to solve partially or globally this problem, allowing to make appropriate choices.La croissance continue de la demande en énergie électrique présente de plus en plus de défis pour la société. Ce phénomène nécessite de grands efforts pour l’optimisation des décisions à prendre surtout pour la gestion de la distribution de l’énergie électrique qui pose de nombreux problèmes à la société, dus principalement à l’extension du réseau, l’augmentation de la consommation d’énergie électrique et à la gestion en temps réel. Comme le renforcement des réseaux électriques est difficile et coûteux en même temps, il est nécessaire d’opter pour une gestion optimale afin de garantir la satisfaction des clients, réduire les dépenses et augmenter la marge bénéficiaire. Dans ce travail, on proposera quelques différentes méthodes d’optimisation afin de résoudre partiellement ou globalement ce problème et permettre à la société de prendre les choix appropriés

A novel hybrid GA and SVM with PSO feature selection for intrusion detection system

The computer network technologies are evolving fast, and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is the main issue of computing because the numbers of attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, especially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study proposes a new method of intrusion detection that uses support vector machine optimizing by a genetic algorithm. to improve the efficiency of detecting known and unknown attacks, we used a Particle Swarm Optimization algorithm to select the most influential features for learning the classification model