Statistical Segregation Method to Minimize the False Detections During DDoS Attacks

DDoS attack aims at occupying the victim resources so as to defy the legitimate requests from reaching it. Even though the attack traffic is generated in intimidating measures, the attack traffic mostly is disguised as the genuine traffic. Hence most of the mitigation methods cannot segregate the legitimate flows from the attack flows accurately. As the result, legitimate flows have also been filtered while appeasing the DDoS flood. In this paper a statistical segregation method (SSM) has been introduced, which samples the flow in consecutive intervals and then the samples are compared against the attack state condition and sorted with the mean as the parameter, then the correlation analysis is performed to segregate attack flows from the legitimate flows. SSM is compared against various other methods and the blend of segregation methods are identified for alleviating the false detections effectively.