Denial-of-service attack detection based on multivariate correlation analysis

Zhiyuan Tan; Aruna Jamdagni; Xiangjian He; Priyadarsi Nanda; Ren Ping Liu

doi:10.1007/978-3-642-24965-5_85

Denial-of-service attack detection based on multivariate correlation analysis

Zhiyuan Tan^*, Aruna Jamdagni, Xiangjian He, Priyadarsi Nanda, Ren Ping Liu

^*Corresponding author for this work

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

45 Citations (Scopus)

Abstract

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.

Original language	English
Title of host publication	Neural information processing
Subtitle of host publication	18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings
Editors	Bao-Liang Lu, Liqing Zhang, James Kwok
Place of Publication	Heidelberg
Publisher	Springer, Springer Nature
Pages	756-765
Number of pages	10
Volume	Part 3
ISBN (Electronic)	9783642249655
ISBN (Print)	9783642249648
DOIs	https://doi.org/10.1007/978-3-642-24965-5_85
Publication status	Published - 2011
Externally published	Yes
Event	18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Berlin Heidelberg
Volume	7064
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Neural Information Processing, ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11

Keywords

anomaly detection
Denial-of-service attack
Euclidean Distance Map
multivariate correlations

Access to Document

10.1007/978-3-642-24965-5_85

Cite this

Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2011). Denial-of-service attack detection based on multivariate correlation analysis. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural information processing: 18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings (Vol. Part 3, pp. 756-765). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064). Springer, Springer Nature. https://doi.org/10.1007/978-3-642-24965-5_85

Tan, Zhiyuan ; Jamdagni, Aruna ; He, Xiangjian et al. / Denial-of-service attack detection based on multivariate correlation analysis. Neural information processing: 18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings. editor / Bao-Liang Lu ; Liqing Zhang ; James Kwok. Vol. Part 3 Heidelberg : Springer, Springer Nature, 2011. pp. 756-765 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Denial-of-service attack detection based on multivariate correlation analysis",

abstract = "The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.",

keywords = "anomaly detection, Denial-of-service attack, Euclidean Distance Map, multivariate correlations",

author = "Zhiyuan Tan and Aruna Jamdagni and Xiangjian He and Priyadarsi Nanda and Liu, {Ren Ping}",

year = "2011",

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pages = "756--765",

editor = "Bao-Liang Lu and Liqing Zhang and James Kwok",

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Tan, Z, Jamdagni, A, He, X, Nanda, P & Liu, RP 2011, Denial-of-service attack detection based on multivariate correlation analysis. in B-L Lu, L Zhang & J Kwok (eds), Neural information processing: 18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings. vol. Part 3, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7064, Springer, Springer Nature, Heidelberg, pp. 756-765, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24965-5_85

Denial-of-service attack detection based on multivariate correlation analysis. / Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian et al.
Neural information processing: 18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings. ed. / Bao-Liang Lu; Liqing Zhang; James Kwok. Vol. Part 3 Heidelberg: Springer, Springer Nature, 2011. p. 756-765 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064).

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

TY - GEN

T1 - Denial-of-service attack detection based on multivariate correlation analysis

AU - Tan, Zhiyuan

AU - Jamdagni, Aruna

AU - He, Xiangjian

AU - Nanda, Priyadarsi

AU - Liu, Ren Ping

PY - 2011

Y1 - 2011

N2 - The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.

AB - The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.

KW - anomaly detection

KW - Denial-of-service attack

KW - Euclidean Distance Map

KW - multivariate correlations

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SN - 9783642249648

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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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EP - 765

BT - Neural information processing

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PB - Springer, Springer Nature

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Y2 - 13 November 2011 through 17 November 2011

ER -

Tan Z, Jamdagni A, He X, Nanda P, Liu RP. Denial-of-service attack detection based on multivariate correlation analysis. In Lu B-L, Zhang L, Kwok J, editors, Neural information processing: 18th international conference, ICONIP 2011, Shanghai, China, November 13-17, 2011: proceedings. Vol. Part 3. Heidelberg: Springer, Springer Nature. 2011. p. 756-765. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24965-5_85

Denial-of-service attack detection based on multivariate correlation analysis

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