Network Intrusion Detection using Selected Data Mining Approaches: A Review

Due to the rapid progress in network technologies, easy availability of the internet and lower cost of mobile devices with wireless network connection facility, the number of internet users is increasing at an exponential rate now-a-days, so does the number of intrusion. Despite the implausible advancement in Information Technology, Intrusion Detection has remained as one of the biggest challenges encountered by network security specialists. Data mining can play a vital role in addressing this issue. In this paper, some selected data mining algorithms available for Network Intrusion Detection have been reviewed, such asSupport Vector Machine, KNearest Neighbor, Naïve Bayesian Classifier, Decision tree Algorithm (C4.5), Genetic Algorithm, Logistic Regression, Artificial Neural network, K-means clustering, EM algorithm, Fuzzy Logic and Hidden Markov Chain; along with addressing the advantages and disadvantages of each of them. General Terms Network Security, Data Mining Algorithms