Performance Evaluation of Network Intrusion Detection System for Detecting Zero-Day Attacks: SNORT-XSS Algorithm

The main objective of Intrusion Detection and Prevention Systems is to provide a method detecting preventing malicious behaviors in network system minimize the harm caused by attackers. In this article, survey techniques applied for identification classification attacks based on KDD Cup’99 DARPA data set discussed, from open issues new proficient called SNORT-XSS algorithm anticipated implemented that can recognize classify real time intrusions including zero day attacks. For research, SNORT source tool developed CISCO was used describe rules existing collected dataset. Fuzzy Reasoning organize into fuzzy sets reduces true negative false positive rate. advantage Feed Forward Neural Network with Back Propagation Errors Artificial Neuron Networks considered training, validating testing proposed system. experimental results achieved preprocessing anomalous detection rate zero-day or novel were very promising beyond expectations. precision values model 98.93% 98.89% respectively, Probe DoS greater than 98%. almost negligible. It noticed best categorization acquired at epoch numbers 50 55 mean squared error 0.004.