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Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm - 2018

Building An Intrusion Detection System Using A Filter-Based Feature Selection Algorithm

Research Paper on Building An Intrusion Detection System Using A Filter-Based Feature Selection Algorithm

Research Area:  Machine Learning


Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

Intrusion Detection System
Filter-Based Feature Selection Algorithm
Machine Learning
Deep Learning

Author(s) Name:  Mohammed A. Ambusaidi; Xiangjian He; Priyadarsi Nanda and Zhiyuan Tan

Journal name:  IEEE Transactions on Computers

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TC.2016.2519914

Volume Information:  Volume: 65, Issue: 10, Oct. 1 2016,Page(s): 2986 - 2998