Cyber Intrusion Detection System Using Deep Learning Approach

Abstract

Abstract: Technological developments in network communications have led to a remarkable increase in network traffic and an explosion in the use of linked devices across a number of commercial fields. Systems for detecting intrusions that can recognize malicious assaults from traffic data might be useful instruments for protecting company assets from illegal access. This project suggests a two-stage architecture for an intrusion detection system, where an auto encoder (AE) and the grey wolf algorithm (GWO) choose features. It is evaluated using the Bot-Iot and NSL-KDD datasets, yielding better accuracy levels for binary and multiclass attack categorization. This method outperforms the latest intrusion detection techniques in terms of categorization using an ideal selection of traffic characteristics.