An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks

In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.