Hybrid feature selection for supporting lightweight intrusion detection systems
Open Access
- 1 August 2017
- conference paper
- Published by IOP Publishing in Journal of Physics: Conference Series
- Vol. 887 (1) , 012031
Abstract
Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.Keywords
This publication has 11 references indexed in Scilit:
- Building an Intrusion Detection System Using a Filter-Based Feature Selection AlgorithmIEEE Transactions on Computers, 2016
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion DetectionIEEE Communications Surveys & Tutorials, 2016
- A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems2015
- Random Forests2012
- Improving Effectiveness of Intrusion Detection by Correlation Feature SelectionInternational Journal of Mobile Computing and Multimedia Communications, 2011
- A novel feature selection approach for biomedical data classificationJournal of Biomedical Informatics, 2010
- Building Lightweight Intrusion Detection System Based on Random Forest2006
- Fusions of GA and SVM for Anomaly Detection in Intrusion Detection System2005
- Consistency Based Feature Selection2000
- Learning Boolean concepts in the presence of many irrelevant featuresArtificial Intelligence, 1994