Learning-based algorithm for detecting abnormal traffic

Changwoo Nam; Seongjin Ahn; Jinwook Chung

doi:10.1007/11942634_104

Learning-based algorithm for detecting abnormal traffic

Changwoo Nam, Seongjin Ahn, Jinwook Chung

Department of Computer Education

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Modern worm viruses not only tend to promote host attacks, but generate high volumes of traffic and frequently result in network failure. This paper proposes a learning-based algorithm for detecting abnormal traffic, ensuring efficient protection against worm viruses, and promoting network level security. The algorithm identifies abnormal traffic, and learns network level characteristics of this traffic, to prevent in advance factors that may result in network failure. The algorithm presented in this paper was applied to the network system, and simulation results showed that unlike previous network systems, the proposed algorithm more efficiency detects worm viruses, and overall, results in improved network security.

Original language	English
Title of host publication	Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings
Pages	1018-1024
Number of pages	7
DOIs	https://doi.org/10.1007/11942634_104
State	Published - 2006
Event	Int. Workshops on FHPCN 2006, XHPC 2006, S-GRACE 2006, GridGIS 2006, HPC-GTP 2006, PDCE 2006, ParDMCom 2006, WOMP 2006, ISDF 2006, and UPWN 2006, Held in Conjunction with the 4th Int. Symp. on Parallel and Distributed Processing and Appl., SPA 2006 - Sorrento, Italy Duration: 4 Dec 2006 → 7 Dec 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4331 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Int. Workshops on FHPCN 2006, XHPC 2006, S-GRACE 2006, GridGIS 2006, HPC-GTP 2006, PDCE 2006, ParDMCom 2006, WOMP 2006, ISDF 2006, and UPWN 2006, Held in Conjunction with the 4th Int. Symp. on Parallel and Distributed Processing and Appl., SPA 2006
Country/Territory	Italy
City	Sorrento
Period	4/12/06 → 7/12/06

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10.1007/11942634_104

Cite this

Nam, C., Ahn, S., & Chung, J. (2006). Learning-based algorithm for detecting abnormal traffic. In Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings (pp. 1018-1024). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4331 LNCS). https://doi.org/10.1007/11942634_104

Nam, Changwoo ; Ahn, Seongjin ; Chung, Jinwook. / Learning-based algorithm for detecting abnormal traffic. Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings. 2006. pp. 1018-1024 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Modern worm viruses not only tend to promote host attacks, but generate high volumes of traffic and frequently result in network failure. This paper proposes a learning-based algorithm for detecting abnormal traffic, ensuring efficient protection against worm viruses, and promoting network level security. The algorithm identifies abnormal traffic, and learns network level characteristics of this traffic, to prevent in advance factors that may result in network failure. The algorithm presented in this paper was applied to the network system, and simulation results showed that unlike previous network systems, the proposed algorithm more efficiency detects worm viruses, and overall, results in improved network security.",

author = "Changwoo Nam and Seongjin Ahn and Jinwook Chung",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings",

note = "Int. Workshops on FHPCN 2006, XHPC 2006, S-GRACE 2006, GridGIS 2006, HPC-GTP 2006, PDCE 2006, ParDMCom 2006, WOMP 2006, ISDF 2006, and UPWN 2006, Held in Conjunction with the 4th Int. Symp. on Parallel and Distributed Processing and Appl., SPA 2006 ; Conference date: 04-12-2006 Through 07-12-2006",

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Nam, C, Ahn, S & Chung, J 2006, Learning-based algorithm for detecting abnormal traffic. in Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4331 LNCS, pp. 1018-1024, Int. Workshops on FHPCN 2006, XHPC 2006, S-GRACE 2006, GridGIS 2006, HPC-GTP 2006, PDCE 2006, ParDMCom 2006, WOMP 2006, ISDF 2006, and UPWN 2006, Held in Conjunction with the 4th Int. Symp. on Parallel and Distributed Processing and Appl., SPA 2006, Sorrento, Italy, 4/12/06. https://doi.org/10.1007/11942634_104

Learning-based algorithm for detecting abnormal traffic. / Nam, Changwoo; Ahn, Seongjin; Chung, Jinwook.
Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings. 2006. p. 1018-1024 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4331 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ahn, Seongjin

AU - Chung, Jinwook

PY - 2006

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N2 - Modern worm viruses not only tend to promote host attacks, but generate high volumes of traffic and frequently result in network failure. This paper proposes a learning-based algorithm for detecting abnormal traffic, ensuring efficient protection against worm viruses, and promoting network level security. The algorithm identifies abnormal traffic, and learns network level characteristics of this traffic, to prevent in advance factors that may result in network failure. The algorithm presented in this paper was applied to the network system, and simulation results showed that unlike previous network systems, the proposed algorithm more efficiency detects worm viruses, and overall, results in improved network security.

AB - Modern worm viruses not only tend to promote host attacks, but generate high volumes of traffic and frequently result in network failure. This paper proposes a learning-based algorithm for detecting abnormal traffic, ensuring efficient protection against worm viruses, and promoting network level security. The algorithm identifies abnormal traffic, and learns network level characteristics of this traffic, to prevent in advance factors that may result in network failure. The algorithm presented in this paper was applied to the network system, and simulation results showed that unlike previous network systems, the proposed algorithm more efficiency detects worm viruses, and overall, results in improved network security.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - Int. Workshops on FHPCN 2006, XHPC 2006, S-GRACE 2006, GridGIS 2006, HPC-GTP 2006, PDCE 2006, ParDMCom 2006, WOMP 2006, ISDF 2006, and UPWN 2006, Held in Conjunction with the 4th Int. Symp. on Parallel and Distributed Processing and Appl., SPA 2006

Y2 - 4 December 2006 through 7 December 2006

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Nam C, Ahn S, Chung J. Learning-based algorithm for detecting abnormal traffic. In Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Proceedings. 2006. p. 1018-1024. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11942634_104

Learning-based algorithm for detecting abnormal traffic

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