Visual analysis and detection of network flood attacks through Two-Layer density approach

Huang, ML; Zhang, J

Visual analysis and detection of network flood attacks through Two-Layer density approach

Huang, ML

Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, ICCSNT 2013, 2014, pp. 625 - 629
Issue Date:: 2014-11-25

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Field	Value	Language
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.date.issued	2014-11-25	en_US
dc.identifier.citation	Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, ICCSNT 2013, 2014, pp. 625 - 629	en_US
dc.identifier.isbn	9781479905614	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34607
dc.description.abstract	© 2013 IEEE. Flood attack patterns have variability depending on the network environment. It has been necessitated that the need for visual analysis within an Intrusion Detection System (IDS) is to identify these flood-attack patterns. The challenges are how to increase the accuracy of detection and how to visualize and present flood attack patterns in networks for early detection. In this paper, we propose a Two-Layer density model for flood attack detection. The first density layer describes sending-density and receiving-density in analyzing Internet traffic. The second density layer describes attack-density and normal-density in analyzing local network traffic at a victim site. Several visualization techniques are used to facilitate the detection process. The experiments demonstrate that the Two-Layer density model has significantly improved the accuracy of the detection of flood attacks and provides users with a better understanding of the nature of flood attacks.	en_US
dc.relation.ispartof	Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, ICCSNT 2013	en_US
dc.relation.isbasedon	10.1109/ICCSNT.2013.6967191	en_US
dc.title	Visual analysis and detection of network flood attacks through Two-Layer density approach	en_US
dc.type	Conference Proceeding
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	080499 Data Format not elsewhere classified	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
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 Computer Science
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:

© 2013 IEEE. Flood attack patterns have variability depending on the network environment. It has been necessitated that the need for visual analysis within an Intrusion Detection System (IDS) is to identify these flood-attack patterns. The challenges are how to increase the accuracy of detection and how to visualize and present flood attack patterns in networks for early detection. In this paper, we propose a Two-Layer density model for flood attack detection. The first density layer describes sending-density and receiving-density in analyzing Internet traffic. The second density layer describes attack-density and normal-density in analyzing local network traffic at a victim site. Several visualization techniques are used to facilitate the detection process. The experiments demonstrate that the Two-Layer density model has significantly improved the accuracy of the detection of flood attacks and provides users with a better understanding of the nature of flood attacks.

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http://hdl.handle.net/10453/34607