Fuzzy Vector Quantization for Network Intrusion Detection

Dat Tran; Wanli Ma; Dharmendra Sharma; Thien Nguyen

doi:10.1109/GrC.2007.124

Fuzzy Vector Quantization for Network Intrusion Detection

Dat Tran, Wanli Ma, Dharmendra Sharma, Thien Nguyen

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

10 Citations (Scopus)

13 Downloads (Pure)

Abstract

This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates

Original language	English
Title of host publication	Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007)
Editors	T.Y Lin, X Hu
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	566-570
Number of pages	5
ISBN (Print)	9780769530321
DOIs	https://doi.org/10.1109/GrC.2007.124
Publication status	Published - 2007
Event	IEEE International Conference on Granular Computing - San Jose, United States Duration: 2 Nov 2007 → 4 Nov 2007

Conference

Conference	IEEE International Conference on Granular Computing
Country/Territory	United States
City	San Jose
Period	2/11/07 → 4/11/07

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10.1109/GrC.2007.124

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@inproceedings{a5ca861d95634476b35d987802a93eea,

title = "Fuzzy Vector Quantization for Network Intrusion Detection",

abstract = "This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates",

author = "Dat Tran and Wanli Ma and Dharmendra Sharma and Thien Nguyen",

year = "2007",

doi = "10.1109/GrC.2007.124",

language = "English",

isbn = "9780769530321",

pages = "566--570",

editor = "T.Y Lin and X Hu",

booktitle = "Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "IEEE International Conference on Granular Computing ; Conference date: 02-11-2007 Through 04-11-2007",

}

Tran, D , Ma, W , Sharma, D & Nguyen, T 2007, Fuzzy Vector Quantization for Network Intrusion Detection. in TY Lin & X Hu (eds), Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 566-570, IEEE International Conference on Granular Computing, San Jose, United States, 2/11/07. https://doi.org/10.1109/GrC.2007.124

Fuzzy Vector Quantization for Network Intrusion Detection. / Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra et al.

Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007). ed. / T.Y Lin; X Hu. United States : IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 566-570.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Fuzzy Vector Quantization for Network Intrusion Detection

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

AU - Nguyen, Thien

PY - 2007

Y1 - 2007

N2 - This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates

AB - This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates

U2 - 10.1109/GrC.2007.124

DO - 10.1109/GrC.2007.124

M3 - Conference contribution

SN - 9780769530321

SP - 566

EP - 570

BT - Proceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007)

A2 - Lin, T.Y

A2 - Hu, X

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - IEEE International Conference on Granular Computing

Y2 - 2 November 2007 through 4 November 2007

ER -

Fuzzy Vector Quantization for Network Intrusion Detection

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