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A Novel Feature Selection Approach for Intrusion Detection Data Classification

Ambusaidi, Mohammed A.; He, Xiangjian; Tan, Zhiyuan; Nanda, Priyadarsi; Lu, Liang Fu; Nagar, Upasana T.


Mohammed A. Ambusaidi

Xiangjian He

Priyadarsi Nanda

Liang Fu Lu

Upasana T. Nagar


Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other stateof-the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the previously reported results.

Presentation Conference Type Conference Paper (Published)
Conference Name 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications
Start Date Sep 24, 2014
End Date Sep 26, 2014
Publication Date 2014-09
Deposit Date Nov 30, 2016
Pages 82-89
ISBN 9781479965137
Keywords Feature extraction, Redundancy, Accuracy, Support vector machines, Mutual information, Training, Intrusion detection
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