The Detection And Defending Of DDoS Attack

Nowadays Distributed Deny Of Service (DDoS) attack have become to be one of the greatest troubles in Network Security. There seem to be no substantial improvement in anti-DDoS research on attack preventing, detecting & retorting yet, nor did any effective or nicety method appear to predict the DDoS attack in time. DDoS attack detection and defending is one of the frontiers in the field of Network security.The traditional Detecting DDoS method detects the DDoS attack based on matching the signature of the attcks, but it is not appropriate for detection of the DDoS attack based on network. In present dissertation, we analyze the statistical characteristic of the real network traffic, experiment the DDoS attack, and measurethe change of the statistical characteristic brought by the attacks. We do researches on constructing normal model of network traffic, analysizing Self-Similarity of network traffics-Hurst Parameter, and its time variable function H(t). Experimental analysis confirmed the validity of the novel mechanism, limiting the extent of network traffic in time and detecting the DDoS attack through measuring the change of H Parameter brought by the attacks. Moreover we use Database to refine the DDoS attack. All these results indicate that statistical analysis based on the network traffic can detected DDoS attack and is more reliable on the recognition of kinds of DDoS attack than any other traditional method based on character recognition.The systematic investigation of defending the DDoS attack is also made. We bring forward and implement several mechanism defending the DDoS attack. The three methods including packet filter, traceback and pushback mechanism are integrated to form a rounded scheme detecting and defending the DDoS attack.