A novel feature selection approach for intrusion detection data classification

Ambusaidi, MA; He, X; Tan, Z; Nanda, P; Lu, LF; Nagar, UT

A novel feature selection approach for intrusion detection data classification

Ambusaidi, MA He, X

Tan, Z

Nanda, P

Lu, LF Nagar, UT

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014, 2015, pp. 82 - 89
Issue Date:: 2015-01-15

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Field	Value	Language
dc.contributor.author	Ambusaidi, MA	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Tan, Z https://orcid.org/0000-0001-5420-2554	en_US
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X	en_US
dc.contributor.author	Lu, LF	en_US
dc.contributor.author	Nagar, UT	en_US
dc.date.issued	2015-01-15	en_US
dc.identifier.citation	Proceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014, 2015, pp. 82 - 89	en_US
dc.identifier.isbn	9781479965137	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30444
dc.description.abstract	© 2014 IEEE. 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.	en_US
dc.relation.ispartof	Proceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014	en_US
dc.relation.isbasedon	10.1109/TrustCom.2014.15	en_US
dc.title	A novel feature selection approach for intrusion detection data classification	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
dc.location.activity	Beijing
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. 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.

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http://hdl.handle.net/10453/30444