HIGH ACCURACY DETECTION OF DENIAL OF SERVICE ATTACK BASED ON TRIANGLE MAP GENERATION?
J.WELKIN EYES, S.KARTHIPREM, E.THANGADURAI?
Journal Title:International Journal of Computer Science and Mobile Computing - IJCSMC
A DoS attack is the most prevalent threat, viz., traffic in communication resources in order to make the service unavailable for legitimate users, since a decade and continues to be threatening. Denial-of-Service (DoS) attacks cause serious impact on these computing systems. In this project, neuro-fuzzy systems were proposed as subsystems of the ensemble. Sugeno type Neuro-Fuzzy Inference System has been chosen as a base classifier for our research. Single classifier makes error on different training samples. So, by creating the classifiers and combining their outputs, the total amount of error can be reduced and the detection accuracy can be increased. The proposed Adaptive Neuro-Fuzzy Inference based system will be able to detect an intrusion behavior of the networks. The experiments and the evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection Dataset. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.