Application Of Wavelet Transform In Network Traffic

The traffic behaivours influence network modeling, service providing and traffic engineering, So it has been the focus of many researches. Since the seminal study of Leland, Taqqu, Willinger, and Wilson who pointed out the existence of scaling behavior in the network traffic, the so-called self-similarity, there is now ample evidence that scale-invariant burstiness is an ubiquitous phenomenon in a wide range of generalized data types, from local-area and wide-area networks to IP and ATM protocol stacks to copper and fiber optic transmission media. Hurst parameter is the key value of this model representing the burstiness of traffic.An adaptive, efficient unbiased estimator of Hurst parameter based on the lifting scheme for wavelet transform and correlation coefficient is presented. Compared with the existing wavelet-based estimator, the new method performs inplace computation and reduces the computational complexity by about half. Simulation results based on fractal Gaussian noise and real traffic data reveal the proposed approach shows more adaptiveness, accuracy and robustness than traditional estimators. Thus this estimator can be applied to the application of traffic management and real-time control in high-speed networks.Defending against Distributed Denial of Service attacks is one of the hardest security problems in the Internet today. The important reason for it is that a vast number of insecure machines exist in the Internet, attack tools can easily be gained and the attacks often use spoofed IP source address. So,it is of importance to detect the occurring of DDoS attacks with high accuracy and rapidity.Attacked by DDoS, the self-similarity in network traffic will be changed, Hurst parameter is the key value representing the self-similarity, using this characteristic for detection of DDoS attack is of efficiency. In this paper,DDoS is simulated in NS-2, different samples of traffic are analyzed based on wavelet. The relationship between sampling methods and estimation of Hurst parameter is presented, and the cause is demonstrated.Simultaneously, by employing sliding windows, a real-time estimator of Hurst parameter based on the lifting scheme for wavelet transform is presented. Analyzing the relation between network traffic sample and Hurst paremeter on the simulation data of NS2,the method of detecting DDoS attack is proposed. Compared with traditional methods, The proposed approach shows real time and efficiency.