An Intrusion Detection System Against DDoS Attacks in IoT Networks

Abstract

In this paper, we present an Intrusion Detection System (IDS) using the hybridization of the deep learning technique and the multi-objective optimization method for the detection of Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT) networks is proposed in this paper. IoT networks consist of different devices with unique hardware and software configurations communicating over different communication protocols, which produce huge multidimensional data that make IoT networks susceptible to cyber-attacks. In a network the IDS is a vital tool for securing it from cyber-attacks. Detection of new emerging cyber threats are becoming difficult for existing IDS, and therefore advanced IDS is required. A DDoS attack is a cyber-attack that has posed substantial devastating losses in IoT networks recently. In this paper, we propose an IDS founded on the fusion of a Jumping Gene adapted NSGA-II multi-objective optimization method for data dimension reduction and the Convolutional Neural Network (CNN) integrating Long Short-Term Memory (LSTM) deep learning techniques for classifying the attack. The experimentation is conducted using a High-Performance Computer (HPC) on the latest CISIDS2017 datasets on DDoS attacks and achieved an accuracy of 99.03% with a 5-fold reduction in training time. We evaluated our proposed method by comparing it with other state-of-the-art algorithms and machine learning algorithms, which confirms that the proposed method surpasses other approaches.