Sequential Markov Model Based Change Point Analysis

Distributed denial of service attacks has become a popular attack for deploying internet crimes. Although patterns, labels, training dataset based detection techniques are accurate, they could be useless when high ooding attacks are encountered. Therefore, technical and mathematical approach for Markov model based Internet trace analysis become attractive due to their ability to detect ooding attacks and even heavy unknown oods. In this paper, we propose a Markov model based on internet trac analysis. We intend to detect the unusual attack behavior changes by inspecting Serial Markovian.. Our proposed method uses Binomial Distribution (BD) approach to calculate Window Size based on state and transition structures of Markov model. It analyzes the Inter Packet Time (IPT) and Packet Size (PS) and shows the good results in terms of Lognormal Distribution and Binomial Distribution. Performance evaluation results based on simulated trac traces shows that the proposed method can reduce more than 85 percentage input raw packet traces and achieve a high detection rate (about 95 percentage) and a low false positive rates (1.08 Percentage).

Autorentext

I'm Surenthar and I'm a master degree holder in computer science. As my zest is around computer networks and its related technologies, I have done vast research in this space and have presented many papers in this field too. My current research is related to Internet traffic analysis and reduction.