Non-linear analytic prediction of IP addresses for supporting cyber attack detection and analysis

Abstract

Computer network systems are often subject to several types of attacks. For example the distributed denial of service (DDoS) attack introduces an excessive traffic load to a web server to make it unusable. A popular method for detecting attacks is to use the sequence of source IP addresses to detect possible anomalies. With the aim of predicting the next IP address, the probability density function of the IP address sequence is estimated. Prediction of source IP address in the future access to the server is meant to detect anomalous requests. In this paper we consider the sequence of IP addresses as a numerical sequence and develop the nonlinear analysis of the numerical sequence. We used nonlinear analysis based on Volterra's Kernels and Hammerstein's models. The experiments carried out with datasets of source IP address sequences show that the prediction errors obtained with Hammerstein models are smaller than those obtained both with the Volterra kernels and with the sequence clustering by means of the k-means algorithm.