Detecting DDoS Attack in Spam Emails using Density-Weight Model

Zhang, J; Huang, M; Hoang, DB

Detecting DDoS Attack in Spam Emails using Density-Weight Model

Zhang, J

Huang, M

Hoang, DB

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Publisher:: IEEE Press
Publication Type:: Conference Proceeding
Citation:: Volume II, Proceedings of 2011 IEEE International Conference on Information Theory and Information Security, 2011, pp. 344 - 352
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.contributor.author	Huang, M https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Hoang, DB https://orcid.org/0000-0003-1798-4926	en_US
dc.contributor.editor	Yang, Y	en_US
dc.contributor.editor	etc	en_US
dc.date	2011-11-04	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	Volume II, Proceedings of 2011 IEEE International Conference on Information Theory and Information Security, 2011, pp. 344 - 352	en_US
dc.identifier.isbn	978-1-61284-742-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29567
dc.description.abstract	DDoS attacks whose are embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The uses of these intrusion detection systems have given rise to two new challenges, 1) how to incrase the accuracy of detection, 2) how to present large spam email networks for better understanding. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight' to measure the network density and system workload for the analysis of DDoS attack of spam emails. We then use a visual clustering method to classify and display the spam emails for better understanding of the spam email network. The experiment shows that the proposed new model can increase the accuracy of the detection of DDoS attacks.	en_US
dc.publisher	IEEE Press	en_US
dc.relation.ispartof	Volume II, Proceedings of 2011 IEEE International Conference on Information Theory and Information Security	en_US
dc.relation.ispartof	IEEE International Conference on Information Theory and Information Security	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Detecting DDoS Attack in Spam Emails using Density-Weight Model	en_US
dc.type	Conference Proceeding
utslib.location	Beijing, China	en_US
utslib.location.activity	Hangzhou, China	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Hangzhou, China	en_US
dc.location.activity	Manchester
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2011-11-04	en_US
pubs.place-of-publication	Beijing, China	en_US
pubs.start-date	2011-11-04	en_US

Abstract:

DDoS attacks whose are embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The uses of these intrusion detection systems have given rise to two new challenges, 1) how to incrase the accuracy of detection, 2) how to present large spam email networks for better understanding. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight' to measure the network density and system workload for the analysis of DDoS attack of spam emails. We then use a visual clustering method to classify and display the spam emails for better understanding of the spam email network. The experiment shows that the proposed new model can increase the accuracy of the detection of DDoS attacks.

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

http://hdl.handle.net/10453/29567