Session:

Hybrid System Applications
Sunday, February 29, 2004, 11.20 – 11.40

Session Chair:

Fikret Gürgen

Paper Title:

Network-Based Intrusion Detection Using Unsupervised Adaptive Resonance Theory (ART)

Author(s):

M. Amini, Sharif University of Technology, Tehran, Iran
R. Jalili, Sharif University of Technology, Tehran, Iran

Abstract:

This paper introduces the Unsupervised Neural Net based Intrusion Detector (UNNID) system, which detects network-based intrusions and attacks using unsupervised neural networks. The system has facilities for training, testing, and tunning of unsupervised nets to be used in intrusion detection. Using the system, we tested two types of unsupervised Adaptive Resonance Theory (ART) nets (ART-1 and ART-2). Based on the results, such nets can efficiently classify network traffic into normal and intrusive. The system uses a hybrid of misuse and anomaly detection approaches, so is capable of detecting known attack types as well as new attack types as anomalies.