Distributed Denial of Service (DDoS), is a coordinated attack majorly carried out on a massive scale against the availability of services/resources of a target system. Several DDoS attack detection, prevention or prediction techniques have been proposed. Some of these techniques have shortcomings such as high false positive rate, high computational time, low prediction precision and so on. This paper presents a novel machine learning technique based on variational Bayesian algorithms to obtain an Hidden Markov Model (HMM) with optimised number of model states and parameters for DDoS attack prediction. This method not only overcomes the slow convergence speed of the HMM approach, but it also avoids the problem of overfitting the model structure by removing excess transition and emission processes. Experiments with the DARPA 2000 intrusion datasets shows this method is able to find the optimal topology in every case and achieves better average precision rate compared to classic HMM.
DDoS, Variational Bayesian, Hidden Markov model, network attacks
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