Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System
Yuna Sugianela, Tohari Ahmad
Abstract
IDS helps to overcome the network attack by taking appropriate preventive measures. The data mining method has good adaptability to new attack types; however, it consumes much time for high dimensional data. Therefore, the system needs a reduction of that high dimension. In this paper, we use a correlation approach of the attribute to evaluate those high dimensional data. To achieve a better environment, we propose a cut-off value of correlation to select some best features to use in the classification process. The best cut-off value in our experiment is 0.2 in RF classification that reaches 99.36% accuracy. The selection feature can reduce the time consumed in the running system.
Topics & Concepts
Intrusion detection systemComputer scienceAdaptabilityFeature selectionData miningCorrelationFeature (linguistics)Artificial intelligenceSelection (genetic algorithm)Dimension (graph theory)Process (computing)IntrusionFeature extractionPattern recognition (psychology)Machine learningMathematicsGeochemistryPure mathematicsPhilosophyBiologyGeometryEcologyOperating systemLinguisticsGeologyNetwork Security and Intrusion DetectionSpam and Phishing DetectionNetwork Packet Processing and Optimization