期刊
DEFENCE SCIENCE JOURNAL
卷 66, 期 6, 页码 612-617出版社
DEFENCE SCIENTIFIC INFORMATION DOCUMENTATION CENTRE
DOI: 10.14429/dsj.66.10802
关键词
Intrusion detection systems; rough set theory; hypergraph; feature selection; kappa-Helly property
资金
- IBM, India
- Department of Science and Technology - Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India [SR/FST/MSI-107/2015, SR/FST/ETI-349/2013]
Immense growth in network-based services had resulted in the upsurge of internet users, security threats and cyber-attacks. Intrusion detection systems (IDSs) have become an essential component of any network architecture, in order to secure an IT infrastructure from the malicious activities of the intruders. An efficient IDS should be able to detect, identify and track the malicious attempts made by the intruders. With many IDSs available in the literature, the most common challenge due to voluminous network traffic patterns is the curse of dimensionality. This scenario emphasises the importance of feature selection algorithm, which can identify the relevant features and ignore the rest without any information loss. A novel rough set kappa-Helly property technique (RSKHT) feature selection algorithm had been proposed to identify the key features for network IDSs. Experiments carried using benchmark KDD cup 1999 dataset were found to be promising, when compared with the existing feature selection algorithms with respect to reduct size, classifier's performance and time complexity. RSKHT was found to be computationally attractive and flexible for massive datasets.
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