Intrusion detection using GSAD model for HTTP traffic on web services

Jamdagni, A; Tan, Z; Nanda, P; He, X; Liu, RP

Intrusion detection using GSAD model for HTTP traffic on web services

Jamdagni, A Tan, Z Nanda, P

He, X

Liu, RP

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Publication Type:: Conference Proceeding
Citation:: IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, 2010, pp. 1193 - 1197
Issue Date:: 2010-08-06

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Field	Value	Language
dc.contributor.author	Jamdagni, A	en_US
dc.contributor.author	Tan, Z	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	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.date.issued	2010-08-06	en_US
dc.identifier.citation	IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, 2010, pp. 1193 - 1197	en_US
dc.identifier.isbn	9781450300629	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14163
dc.description.abstract	Intrusion detection systems are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. Hypertext Transport Protocol (HTTP) is used for new applications without much interference. In this paper, we focus on intrusion detection of HTTP traffic by applying pattern recognition techniques using our Geometrical Structure Anomaly Detection (GSAD) model. Experimental results reveal that features extracted from HTTP request using GSAD model can be used to distinguish anomalous traffic from normal traffic, and attacks carried out over HTTP traffic can be identified. We evaluate and compare our results with the results of PAYL intrusion detection systems for the test of DARPA 1999 IDS data set. The results show GSAD has high detection rates and low false positive rates. Copyright © 2010 ACM.	en_US
dc.relation.ispartof	IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference	en_US
dc.relation.isbasedon	10.1145/1815396.1815669	en_US
dc.title	Intrusion detection using GSAD model for HTTP traffic on web services	en_US
dc.type	Conference Proceeding
utslib.for	1005 Communications Technologies	en_US
dc.location.activity	Caen, France	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Intrusion detection systems are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. Hypertext Transport Protocol (HTTP) is used for new applications without much interference. In this paper, we focus on intrusion detection of HTTP traffic by applying pattern recognition techniques using our Geometrical Structure Anomaly Detection (GSAD) model. Experimental results reveal that features extracted from HTTP request using GSAD model can be used to distinguish anomalous traffic from normal traffic, and attacks carried out over HTTP traffic can be identified. We evaluate and compare our results with the results of PAYL intrusion detection systems for the test of DARPA 1999 IDS data set. The results show GSAD has high detection rates and low false positive rates. Copyright © 2010 ACM.

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http://hdl.handle.net/10453/14163