Flow Based Intrusion Detection System Using Multistage Neural Network

Abstract

With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. And to keep security at highest level, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit. In the recent years many researchers focus their hard work on this field using different approaches to build dependable intrusion detection systems. One of these approaches is Flow-based intrusion detection systems that rely on aggregated network traffic flows. In this paper, Multistage Neural Network intrusion detection system based on aggregated flow data is proposed for detecting and classifying attacks in network traffic. The proposed system detects significant changes in the traffic that could be a possible attack in the first stage of neural network, while the second stage has the ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Two different neural network structures with the use of different training algorithms have been used in our proposed Intrusion Detection System. The experimental results show that the designed system is promising in terms of accuracy and low probability of false alarms, where the overall accuracy classification rate average is equal to 99.25%.