An Evolutionary General Regression Neural Network Classifier for Intrusion Detection
J. Brown, Mohd M. Anwar, Gerry Vernon Dozier · 2016
The goal of an intrusion detection system (IDS) is to monitor anomalous activities and differentiate between normal and abnormal behaviors (intrusion) in a host system or in a network. The IDS must maintain a high intrusion detection rate (DR) while simultaneously maintain a low false alarm rate (FAR). A high detection rate is the focus of this paper. In this paper, we implemented an Evolutionary General Regression Neural Network (E-GRNN) as a two-class classifier for intrusion detection based on features of application layer protocols (e.g., http, ftp, smtp, etc.) used in simulated network traffic activities. The E-GRNN is an evolutionary search-inspired General Regression Neural Network, which extracts the most salient features to reduce computational complexity and increase accuracy. Our research shows that the E-GRNN classifier was able to achieve a DR of 95.53% and an FAR of 2.11%.