Genetic programming and K-nearest neighbour classifier based intrusion detection model

In computer networks, Intrusion Detection has become a major concern. In network security, various traditional techniques like intrusion prevention, cryptography and user authentication are unable to detect establishment of novel attacks. An intrusion detection system is helpful in detecting an unusual intruder which cracks into the system or genuine user mistreating the system. Intrusion Detection System continually runs in the background and when any suspicious or obtrusive event occurs then it warns the user. To implement these systems various researchers introduced numerous machine learning techniques like Decision Trees, Support Vector Machines, Artificial Neural Networks, Linear Genetic Programming, Genetic Algorithms, Fuzzy Inference Systems, Rule Based Approach and their ensemble approaches with the intent to predict the data either normal or abnormal. In this paper genetic programming with K-Nearest Neighbor classifier is proposed so as to build an efficient Intrusion Detection Model. Optimal feature selection task is performed by genetic programming whereas the data mining classifier which performs the classification process is K-Nearest Neighbour. The main aim of genetic programming is to aid K-Nearest Neighbour. The experimental result shows that the validation accuracy for detecting attacks is 99.6%.