Intrusion detection systems play an important role in preventing attacks which have been increased rapidly due to the dependence on network and Internet connectivity. Deep learning algorithms are promising techniques, which have been used in many classification problems. In the same way, multi-agent systems become a new useful approach in intrusion detection field. In this paper, we propose a deep learning-based multi-agent system for intrusion detection which combines the desired features of multi-agent system approach with the precision of deep learning algorithms. Therefore, we created a number of autonomous, intelligent and adaptive agents that implanted three algorithms, namely autoencoder, multilayer perceptron and k-nearest neighbors. Autoencoder is used as features reduction tool, and multilayer perceptron and k-nearest neighbors are used as classifiers. The performance of our model is compared against traditional machine learning approaches and other multi-agent system-based systems. The experiments have shown that our hybrid distributed intrusion detection system achieves the detection with better accuracy rate and it reduces considerably the time of detection.
Most current IDS are generally centralized andsuffer from significant limitations when used in high speednetworks, especially when they face distributed attacks. Thispaper shows that the use of mobile agents has practical advantagesfor intrusion detection. For this purpose we carriedout a comparative experimental study of some IDS, showingtheir limits and then we propose an implementation of a newMAFIDS (Mobile Agent for Intrusion Detection System)model focusing on misuse approach. The performance ofMAFIDS is investigated in terms of detection delay, falsealarm and detection rate by comparing it to a centralizedIDS over real traffic and a set of simulated attacks.
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