@inbook{881a257c8ed340e6949290ca9397afd6,
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 = "SCS-Cybersecurity, EWI-25313, Multivariate Correlations, Denial-of-Service Attack, Euclidean Distance Map, Anomaly Detection, IR-92852",
author = "Zhiyuan Tan and Aruna Jamdagni and Xiangjian He and Priyadarsi Nanda and Liu, \{Ren Ping\}",
year = "2011",
month = nov,
doi = "10.1007/978-3-642-24965-5\_85",
language = "Undefined",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "756--765",
editor = "Bao-Liang Lu and Liqing Zhang and James Kwok",
booktitle = "Neural Information Processing",
address = "Germany",
note = "18th International Conference on Neural Information Processing, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",
}