Optimized Intrusion Detection by CACC Discretization Via Naïve Bayes and K-Means Clustering
نویسندگان
چکیده
Network Intrusion Detection System (IDS), as the main security defending technique, is second guard for a network after firewall. Data mining technology is applied to the network intrusion detection, and Precision of the detection will be improved by the superiority of data mining. For IDS many machine learning approaches are ad-acute but they all work efficiently on basis of the training data accuracy. In this paper we used CACC Discretization algorithm to improve the training data representation and then used a crossbreed way by using Naïve Bayes and K-Means clustering. The database Discretization performs well in terms of detecting attacks faster and with reasonable false alarm rate. Index Terms Intrusion Detection System, Discretization, Crossbreed approach, Clustering, Classification.
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