A Hybrid Convolution Neural Network with Binary Particle Swarm Optimization for Intrusion Detection

Abstract—Many companies start using could systems and services to increase their productivity and decrease the cost by migrating their applications, infrastructures, and data to external cloud platforms. Using cloud systems leads to raise the number of attacks on such systems. Protecting these cloud platforms from different attacks becomes an essential task using Intrusion detection systems (IDS). In general, IDS is used to detect normal or abnormal network traffic packets. In this paper, we proposed a hybrid intelligent IDS system based on a onedimensional Convolution Neural Network (1D-CNN) and Binary Particle swarm Optimization (BPSO). BPSO is employed as a wrapper feature selection to determine the most valuable features and reduce the high dimensionality of collected data. While 1DCNN is employed as a binary classifier. We adopted a real dataset called UNSW-NB15 to evaluate the proposed hybrid IDS. The obtained results show the proposed system can detect normal and abnormal packets with an accuracy equals 94:3%