Frequency based DDoS attack detection approach using naive Bayes classification

Being available for their legitimate users is one of the main concerns of web service servers. One of the main threats to availability of servers are DDoS attacks. Flooding the server with bogus packets which leads to overuse the sources of it, a DDoS attack deprives authorized clients of benefits from their services. In order to disguise itself from intrusion detection systems, sophisticated DDoS attack mechanisms are invented whose packets are very similar to those in normal traffics. Frequency domain analysis would be a promising alternative for conventional methods of detection. In this paper we provide a naive Bayes classifier with two frequency based methods of discrete Fourier transform and discrete wavelet transform in order to separate between attack and normal traffics. It founds that, frequency analysis of DDoS attack can result in good performance.