Deep residual convolutional neural Network: An efficient technique for intrusion detection system

Abstract

The fast growth of computer networks over the past few years has made network security in smart cities a significant issue. Network intrusion detection is crucial to maintaining the integrity, confidentiality, and resource accessibility of the various network security rules. Conventional intrusion detection systems frequently use mining association rules to identify intrusion behaviors. They run into issues such as a high false alarm rate (FAR), limited generalization capacity, and slow timeliness because they cannot adequately extract distinctive information about user activities. The primary goal of the current research is to classify attacks using efficient approaches to identify genuine packets. If the number of characteristics in a dataset decreases, the complexity of DL approaches is significantly decreased. In this research work, the Deep Residual Convolutional neural network (DCRNN) is proposed to enhance network security through intrusion detection, which is optimized by the Improved Gazelle Optimization Algorithm (IGOA). Feature selection has eliminated irrelevant features from network data used in obstacle classification processes. Essential features are chosen using the Novel Binary Grasshopper Optimization Algorithm (NBGOA). Experimentation is carried out using the UNSW-NB-15, Cicddos2019 dataset, and CIC-IDS-2017 dataset. According to the experimental findings, the proposed system outperforms existing models regarding classification accuracy and processing time. The results demonstrate that the presented approach efficiently and precisely identifies various assaults.