Network Anomaly Detection System using Deep Learning with Feature Selection Through PSO

Abstract

The more computer systems that communicate and cooperate, the more crucial it is to make our lives simpler. At the same time, it highlights faults that people are unable to correct. Due to faults, cyber-security procedures are required to communicate data. Secure communication requires both the installation of security measures and the development of security measures to address changing security concerns. In this study, it is suggested that network intrusion detection systems be able to adapt and be resilient. This could be done by using deep learning architectures. Deep learning is used in this article to find and group network attacks. There are some tools that can help intrusion detection systems that are more flexible learn to recognise new or zero-day network behaviour features, which can help them get rid of bad guys and make it less likely that they'll get into your network. The model's efficacy was tested using the KDD dataset, which combines real-world network traffic with fake attack operations.