Rule Based Classification Approach towards Detection of Network Intrusions

Network security has become an important is- sue 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 be- hind 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 ap- plying data mining algorithms to intrusion detection database. Performance of various rule based classifi- ers like Part, Ridor, NNge, DTNB, JRip, Conjunctive Rule, One R, Zero R and Decision Table are com- pared and result shows that classification algorithm performs well in terms of accuracy, specificity and sensitivity. The performance of the model is measured using 10- fold cross validation.