Multi stage attack Detection system for Network Administrators using Data Mining

Abstract

In this paper, we present a method to discover, visualize, and predict behavior pattern of attackers in a network based system. We proposed a system that is able to discover temporal pattern of intrusion which reveal behaviors of attackers using alerts generated by Intrusion Detection System (IDS). We use data mining techniques to find the patterns of generated alerts by generating Association rules. Our system is able to stream real-time Snort alerts and predict intrusions based on our learned rules. Therefore, we are able to automatically discover patterns in multistage attack, visualize patterns, and predict intrusions.