LAUSR

Denial of Service (DoS) attacks are prevailing as a significant threat in computer networks necessitating a system to detect DoS attacks for protecting the computing resources. The existing solutions to detect DoS attacks face the lacuna of dimensionality which escalates computational cost and false alarm rate. These issues have been addressed by proposing Class Scatter Ratio (CSR) and Feature Distance Map (FDM) based statistical approach for detecting DoS attacks. CSR calculates the weights of each feature for identifying the best by distance based classifier. FDM extracts the correlation among the features. The attack is detected by comparing the computed FDM of new traffic with normal and attack profile vectors. Three experiments were conducted and the performance evaluation reveals that the computational complexity, false alarm rate, and execution time are low for the proposed approach. Further, it is evident from the ten fold cross validations that the accuracy obtained by the proposed approach for all the datasets are within the 95% confidence interval. Moreover, the proposed statistical approach yields significant results compared to the existing feature selection and extraction techniques and state-of-the-art attack detection systems.