Visualization Technique for Intrusion Detection

Abstract

Intrusion detection system (IDS) plays a vital and crucial role in a computer security. However, they suffer from a number of problems such as low detection of DoS (denial-of-service)/DDoS (distributed denial-of-service) attacks with a high rate of false alarms. In this chapter, a new technique for detecting DoS attacks is proposed; it detects DOS attacks using a set of classifiers and visualizes them in real time. This technique is based on the collection of network parameter values (data packets), which are automatically represented by simple geometric graphs in order to highlight relevant elements. Two implementations for this technique are performed. The first is based on the Euclidian distance while the second is based on KNN algorithm. The effectiveness of the proposed technique has been proven through a simulation of network traffic drawn from the 10% KDD and a comparison with other classification techniques for intrusion detection.