Integrated quantum flow and hidden Markov chain approach for resisting DDoS attack and C-Worm

Abstract

A denial of service attack becomes a major security issue in the network service provisioning. New worm detection methods are not capable to analyze and detect exponentially rising abnormal traffic patterns. In this paper, a new method called an integrated quantum flow and hidden Markov chain approach (IQF-HMC) is introduced in the internet service provisioning. The Quantum flow measured network traffic with features of origination source, nature of the data traffic, and time duration. The standard classes of traffic pattern are evaluated with training sample and the entropy of test traffic data flow patterns are analyzed and compared to detect and resist the abnormal traffic flooding attack. In addition, the quantum phase shift is done on incoming data traffic pattern enables the server to identify the abnormal cross traffic. After that, hidden Markov chain (HMC) is integrated with quantum flow model to restrict the propagation of uncontrolled malicious traffic by camouflage-Worm. Hidden Markov chain adapted a dynamic Bayesian network to evaluate the camouflaging worm propagation with optimal nonlinear filtering. This integrated method improves the performance of the secured network communication in the internet scenario. The performance parameters are DDoS flood attack resistance rate, execution time, true positive rate and memory utilization.