Perspective Analysis of Machine Learning Algorithms for Detecting Network Intrusions

Network security has become an important issue due to the evolution of internet. It brings people not only together but also provides huge potential threats. Intrusion detection technique is considered as the immense method to deploy networks security behind firewalls. An intrusion is defined as a violation of security policy of the system. Intrusion detection systems are developed to detect those violations. Due to the effective data analysis method, data mining is introduced into IDS. This paper brings an idea of applying data mining algorithms to intrusion detection database. Performance of various rule and function based classifiers like Part, Ridor, NNge, DTNB, JRip, Conjunctive Rule, One R, Zero R, Decision Table, RBF, Multi Layer Perception and SMO algorithms are compared and result shows that SMOciassification algorithm performs well in terms of accuracy, specificity and sensitivity. The performance of the model is measured using 10-fold cross validation.