A statistical class center based triangle area vector method for detection of denial of service attacks

Denial of service (DoS) attack is the menace to private cloud computing environment that denies services provided by cloud servers leading to huge business losses. Efficient DoS attack detection mechanisms are demanded which necessitates the extraction of features for its best performance. The lacuna in the existing feature extraction based detection systems is the sensitiveness of initial cluster center which leads to high false alarm rate and low accuracy. In this paper, this issue is addressed by proposing a class center based triangle area vector (CCTAV) method which computes the mean of target classes individually and extracts the correlation between features. Mahalanobis distance measure is used for profile construction and DoS attacks detection. The proposed CCTAV method is tested with five publicly available datasets and compared with existing methods. It is noticed that the proposed statistical method reduces the complexity of feature extraction and enhances the attack detection process. The proposed approach is evaluated by conducting tenfold cross validation to compute 95% confidence interval. It is evident that the accuracy obtained for all the datasets are within the confidence interval. Further, the proposed CCTAV method provides significant results compared to the state-of-the-art attack detection methods.