Detection and Classification Of Normal and Anomaly IP Packet

Intrusion detection system is a vital part of computer security system commonly used for precaution and detection. It is built for classifier or descriptive or predictive model to proficient classification of normal behavior from abnormal behavior of IP packets. This book presents the solution regarding proper data transformation methods handling and importance of data analysis of complete data set which is apply on hybrid neural network approaches for used to cluster and classify normal and abnormal behavior to improve the accuracy of network based anomaly detection classifier. Because neural network classes only require the numerical form of data but IP connections or packets of network have some symbolic features which are difficult to handle without the proper data transformation analysis. For this reason, it got non redundant new NSL KDD CUP data set. The experimental results show that indicator variable is more effective as compared to the both conditional probabilities and arbitrary assignment method from measurement of accuracy and balance error rate.