Hybrid rat swarm hunter prey optimization trained deep learning for network intrusion detection using CNN features

Abstract

In recent years, the emerging growth of various network technologies, namely the cloud computing, 5 G networks, Internet of Things (IoT), and so on resulted in the createion of huge quantity of data and creates challenges to the security of networks. The classical firewalls failed to meet various requirements of the current security of the networks due to the increasing number of network attacks. Hence, an optimization-enabled deep learning model Rat Swarm Hunter Prey Optimization-Deep Maxout Network (RSHPO-DMN) technique is designed to effectively handle various network threats. The Z-score data normalization is applied for data pre-processing initially and by considering chord distance, the data is transformed into usable formats. The transformed data are extracted using the Convolutional Neural Network (CNN) feature and the extracted feature is converted into vector format for network intrusion detection process. The DMN is used for intrusion detection in networks and the designed RSHPO model is used to boost the intrusion detection rate exhibited in the classifier. The RSHPO-DMN model achieved superior performance under different evaluation indicators with accuracy of 90.88 %, precision of 93.58 %, recall of 96.54 %, and f1-score of 95.04 % respectively than other prevailing intrusion detection approaches.