An Evolutionary General Regression Neural Network Classifier for Intrusion Detection

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%.