Unsupervised anomaly intrusion detection using ant colony clustering model

Wilson TSANG; Sam KWONG

doi:10.1007/3-540-32391-0

Unsupervised anomaly intrusion detection using ant colony clustering model

Wilson TSANG
, Sam KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

6 Citations (Scopus)

Abstract

In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for op-timal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.

Original language	English
Title of host publication	Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05
Editors	Ajith ABRAHAM, Yasuhiko DOTE, Takeshi FURUHASHI, Mario KÖPPEN, Azuma OHUCHI, Yukio OHSAWA
Publisher	IEEE
Pages	223-232
Number of pages	10
ISBN (Electronic)	9783540323914
ISBN (Print)	9783540250555
DOIs	https://doi.org/10.1007/3-540-32391-0
Publication status	Published - 2005
Externally published	Yes
Event	4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology - Muroran, Japan Duration: 25 May 2005 → 27 May 2005

Workshop

Workshop	4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology
Abbreviated title	WSTST 2005
Country/Territory	Japan
City	Muroran
Period	25/05/05 → 27/05/05

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10.1007/3-540-32391-0

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TSANG, W., & KWONG, S. (2005). Unsupervised anomaly intrusion detection using ant colony clustering model. In A. ABRAHAM, Y. DOTE, T. FURUHASHI, M. KÖPPEN, A. OHUCHI, & Y. OHSAWA (Eds.), Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05 (pp. 223-232). IEEE. https://doi.org/10.1007/3-540-32391-0

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title = "Unsupervised anomaly intrusion detection using ant colony clustering model",

abstract = "In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for op-timal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.",

author = "Wilson TSANG and Sam KWONG",

year = "2005",

doi = "10.1007/3-540-32391-0",

language = "English",

isbn = "9783540250555",

pages = "223--232",

editor = "Ajith ABRAHAM and Yasuhiko DOTE and Takeshi FURUHASHI and Mario K{\"O}PPEN and Azuma OHUCHI and Yukio OHSAWA",

booktitle = "Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05",

publisher = "IEEE",

note = "4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005 ; Conference date: 25-05-2005 Through 27-05-2005",

}

TSANG, W & KWONG, S 2005, Unsupervised anomaly intrusion detection using ant colony clustering model. in A ABRAHAM, Y DOTE, T FURUHASHI, M KÖPPEN, A OHUCHI & Y OHSAWA (eds), Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05. IEEE, pp. 223-232, 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, Muroran, Japan, 25/05/05. https://doi.org/10.1007/3-540-32391-0

Unsupervised anomaly intrusion detection using ant colony clustering model. / TSANG, Wilson; KWONG, Sam.
Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05. ed. / Ajith ABRAHAM; Yasuhiko DOTE; Takeshi FURUHASHI; Mario KÖPPEN; Azuma OHUCHI; Yukio OHSAWA. IEEE, 2005. p. 223-232.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - TSANG, Wilson

AU - KWONG, Sam

PY - 2005

Y1 - 2005

N2 - In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for op-timal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.

AB - In this paper, we present an efficient and biologically inspired clustering model for anomaly intrusion detection. The proposed model called Ant Colony Clustering Model (ACCM) that improves existing ant-based clustering model in searching for op-timal clustering heuristically. Experimental results on KDD-Cup99 benchmark data show that ACCM is effective to detect known and unseen attacks with high detection rate and low false positive rate.

UR - https://www.scopus.com/pages/publications/33846295620

U2 - 10.1007/3-540-32391-0

DO - 10.1007/3-540-32391-0

M3 - Conference paper (refereed)

SN - 9783540250555

SP - 223

EP - 232

BT - Soft Computing as Transdisciplinary Science and Technology: Proceedings of the fourth IEEE International Workshop WSTST´05

A2 - ABRAHAM, Ajith

A2 - DOTE, Yasuhiko

A2 - FURUHASHI, Takeshi

A2 - KÖPPEN, Mario

A2 - OHUCHI, Azuma

A2 - OHSAWA, Yukio

PB - IEEE

T2 - 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology

Y2 - 25 May 2005 through 27 May 2005

ER -

Unsupervised anomaly intrusion detection using ant colony clustering model

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