Intrusion detection using geometrical structure

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

Intrusion detection using geometrical structure

Jamdagni, A Tan, Z

Nanda, P

He, X

Liu, R

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Publication Type:: Conference Proceeding
Citation:: 4th International Conference on Frontier of Computer Science and Technology, FCST 2009, 2009, pp. 327 - 333
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Jamdagni, A	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	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Liu, R https://orcid.org/0000-0001-7001-6305	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	4th International Conference on Frontier of Computer Science and Technology, FCST 2009, 2009, pp. 327 - 333	en_US
dc.identifier.isbn	9780769539324	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10727
dc.description.abstract	We propose a statistical model, namely Geometrical Structure Anomaly Detection (GSAD) to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile. It calculates weight factor to determine anomaly in the payload. In the 1999 DARPA intrusion detection evaluation data set, we conduct several tests for limited attacks on port 80 and port 25. Our approach establishes and identifies the correlation among packet payloads in a network. © 2009 IEEE.	en_US
dc.relation.ispartof	4th International Conference on Frontier of Computer Science and Technology, FCST 2009	en_US
dc.relation.isbasedon	10.1109/FCST.2009.97	en_US
dc.title	Intrusion detection using geometrical structure	en_US
dc.type	Conference Proceeding
utslib.for	080303 Computer System Security	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Shanghai, China	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:

We propose a statistical model, namely Geometrical Structure Anomaly Detection (GSAD) to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile. It calculates weight factor to determine anomaly in the payload. In the 1999 DARPA intrusion detection evaluation data set, we conduct several tests for limited attacks on port 80 and port 25. Our approach establishes and identifies the correlation among packet payloads in a network. © 2009 IEEE.

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http://hdl.handle.net/10453/10727