A deep learning-based multi-agent system for intrusion detection

Abstract

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.