Intelligent network intrusion detection system using data mining techniques

With the tremendous growth of usage of internet and development in web applications running on various platforms are becoming the major targets of attack. New threats are create everyday by individuals and organizations that attack network systems. Intrusion is a malicious, externally induced operational fault. Intrusion is used as a key to compromise the integrity, availability and confidentiality of a computer resource. Hence intrusion detection systems (IDS) are becoming a key part of system defence, to detect anomalies and attacks in the network. Data mining based IDS can effectively identify intrusions. Average one dependence estimators (AODE) is one of the recent enhancements of naïve bayes algorithm. AODE solves the problem of independence by averaging all models generated by traditional one dependence estimator and is well suited for incremental learning. In this paper, we propose intelligent network intrusion detection system using AODE algorithm for the detection of different types of attacks. In order to evaluate the performance of our proposed system, we conducted experiments on NSL-KDD data set. Empirical results show that proposed model based on AODE is efficient with low FAR and high DR.