Evaluation on multivariate correlation analysis based denial-of-service attack detection system

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

Evaluation on multivariate correlation analysis based denial-of-service attack detection system

Tan, Z Jamdagni, A Nanda, P

He, X

Liu, RP

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Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2012, pp. 160 - 164
Issue Date:: 2012-12-01

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Field	Value	Language
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	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	ACM International Conference Proceeding Series, 2012, pp. 160 - 164	en_US
dc.identifier.isbn	9781450318228	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31371
dc.description.abstract	In this paper, a Denial-of-Service (DoS) attack detection system is explored, where a multivariate correlation analysis technique based on Euclidean distance is applied for network traffic characterization and the principal of anomaly-based detection is employed in attack recognition. The effectiveness of the detection system is evaluated on the KDD Cup 99 dataset and the influence of data normalization on the performance of attack detection is analyzed in this paper as well. The evaluation results and comparisons prove that the detection system is effective in distinguishing DoS attack network traffic from legitimate network traffic and outperforms two state-of-the-art systems. Copyright 2012 ACM.	en_US
dc.relation.ispartof	ACM International Conference Proceeding Series	en_US
dc.relation.isbasedon	10.1145/2490428.2490450	en_US
dc.title	Evaluation on multivariate correlation analysis based denial-of-service attack detection system	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	India
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:

In this paper, a Denial-of-Service (DoS) attack detection system is explored, where a multivariate correlation analysis technique based on Euclidean distance is applied for network traffic characterization and the principal of anomaly-based detection is employed in attack recognition. The effectiveness of the detection system is evaluated on the KDD Cup 99 dataset and the influence of data normalization on the performance of attack detection is analyzed in this paper as well. The evaluation results and comparisons prove that the detection system is effective in distinguishing DoS attack network traffic from legitimate network traffic and outperforms two state-of-the-art systems. Copyright 2012 ACM.

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http://hdl.handle.net/10453/31371