Komparasi Information Gain, Gain Ratio, CFs-Bestfirst dan CFs-PSO Search Terhadap Performa Deteksi Anomali

Abstract

Large data dimensionality is one of the issues in anomaly detection. One approach used to overcome large data dimensions is feature selection. An effective feature selection technique will produce the most relevant features and can improve the classification algorithm to detect attacks. There have been many studies on feature selection techniques, each using different methods and strategies to find the best and relevant features. In this study, a comparison of Information Gain, Gain Ratio, CFs-BestFirst and CFs-PSO Search techniques was compared. The selection features of the four techniques were further validated by the Naive Bayes classification algorithm, k-NN and J48. This study uses the ISCX CICIDS-2017 dataset. Based on the test results the feature selection techniques affect the performance of the Naive Bayes algorithm, k-NN and J48. Increasingly relevant and important features can improve detection performance. The test results also show that the number of features influences the processing / computing time. CFs-BestFirst produces a smaller number of features compared to CFs-PSO Search, Information Gain and Gain Ratio so it requires lower processing time. In addition, k-NN requires a higher processing time than Naive Bayes and J48