An intrusion detection system based on polynomial feature correlation analysis

Li, Q; Tan, Z; Jamdagni, A; Nanda, P; He, X; Han, W

An intrusion detection system based on polynomial feature correlation analysis

Li, Q Tan, Z Jamdagni, A Nanda, P

He, X

Han, W

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017, 2017, pp. 978 - 983
Issue Date:: 2017-09-07

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (602.93 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Li, Q	en_US
dc.contributor.author	Tan, Z	en_US
dc.contributor.author	Jamdagni, A	en_US
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Han, W	en_US
dc.date.issued	2017-09-07	en_US
dc.identifier.citation	Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017, 2017, pp. 978 - 983	en_US
dc.identifier.isbn	9781509049059	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115398
dc.description.abstract	© 2017 IEEE. This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.	en_US
dc.relation.ispartof	Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017	en_US
dc.relation.isbasedon	10.1109/Trustcom/BigDataSE/ICESS.2017.340	en_US
dc.title	An intrusion detection system based on polynomial feature correlation analysis	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
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/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
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115398