Using genetic algorithms to detect intrusions for IoT systems

The purpose of this paper is to develop an intrusion detection system for IoT systems using deep learning and the genetic algorithm to optimize (or, more precisely, compress) the model. To meet the security requirements of IoT systems, we aim to design a powerful IDS system capable of detecting novel attacks, with a lightweight architecture and reduced classification time. We proposed two neural network architecture-based systems: the multi-layer perceptron and the auto-encoder model. The NSL-KDD dataset is utilized for the evaluation of these two systems