Visual analytics model for intrusion detection in flood attack

Zhang, J; Huang, ML

Visual analytics model for intrusion detection in flood attack

Zhang, J

Huang, ML

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, 2013, pp. 277 - 284
Issue Date:: 2013-12-01

Closed Access

	Filename	Description	Size
	2013004170OK.pdf		856.63 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, 2013, pp. 277 - 284	en_US
dc.identifier.isbn	9780769550220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28366
dc.description.abstract	Flood attacks are common forms of Distributed Denial-of-Service (DDoS) attack threats on internet in nature. This has necessitated the need for visual analysis within an intrusion detection system to identify these attacks. The challenges are how to increase the accuracy of detection and how to visualize and present flood attacks in networks for early detection. In this paper, we introduce three coefficients, which not only classify the behaviors of flood attacks, but also measure the system performance under those flood attacks: a) attack-density that patterns the characters of flood attack, b) system workload which represents the system capability in handling flood attack and c) the scalability to classify the impact level of the flood attack at victim site. A visual clustered method is used to display the DDoS flood attacks. The experimentation results are presented to demonstrate our new model significantly improves the accuracy of the detection of DDoS attacks and provides a better understanding of the nature of flood attacks on networks. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013	en_US
dc.relation.isbasedon	10.1109/TrustCom.2013.38	en_US
dc.title	Visual analytics model for intrusion detection in flood attack	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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:

Flood attacks are common forms of Distributed Denial-of-Service (DDoS) attack threats on internet in nature. This has necessitated the need for visual analysis within an intrusion detection system to identify these attacks. The challenges are how to increase the accuracy of detection and how to visualize and present flood attacks in networks for early detection. In this paper, we introduce three coefficients, which not only classify the behaviors of flood attacks, but also measure the system performance under those flood attacks: a) attack-density that patterns the characters of flood attack, b) system workload which represents the system capability in handling flood attack and c) the scalability to classify the impact level of the flood attack at victim site. A visual clustered method is used to display the DDoS flood attacks. The experimentation results are presented to demonstrate our new model significantly improves the accuracy of the detection of DDoS attacks and provides a better understanding of the nature of flood attacks on networks. © 2013 IEEE.

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

http://hdl.handle.net/10453/28366