Feature Dependent Naïve Bayes for Network Intrusion Detection System

Abstract

The intrusion detection system is an important component that performs the analysis for. the problem arising from the IDS is a collection of data sets in a computer network. to increase the high level and low false positive level of approach with the learning machine in applied. The data mining algorithm used is Naïve bayes one of the most widely used algorithms in space due to its simplicity, efficiency and effectiveness. NB has high accuracy and speed when applied into the database with large data. However, the NB algorithm assumes independent attributes (free) and is very sensitive to the selection of many features that interfere with the performance or accuracy of the NBto be low but in practice, the possibilities of the feature are interrelated. The Feature DependentNaïve Bayes (FDNB) method is an effective method used to solve existing problems in NB by computing features as pairs and creating dependencies between each other as well as by applying learning models implemented to cross-validation, Feature Selection and data steps preprocessing that gives better accuracy results. After testing with two models of Naïve bayes and FDNB, the results obtained from the Naïve Bayes algorithm resulted in an accuracy of 84.42%, while for FDNB and oversampling (CFS + GS) the accuracy was 94.58%, FDNB and oversampling (CFS + BFS) the accuracy value of 94.69%, FDNB and SMOTE (CFS + GS) and FDNB and SMOTE (CFS + BFS) has an accuracy value of 93.27%. For the average per attack type DOS attack shows the highest result for its accuracy value of 97.86% and U2R attack produces the best accuracy when classifying U2R with 93.80% accuracy, U-F sizeof 96.26% U2R can be considered as a very result nice. Because U2R attack is considered very dangerous.