EEMCS EPrints Service
Tan, Zhiyuan and Jamdagni, Aruna and He, Xiangjian and Nanda, Priyadarsi and Liu, Ren Ping (2011) Denial-of-service attack detection based on multivariate correlation analysis. In: Neural Information Processing. Lecture Notes in Computer Science 7064. Springer Verlag, Berlin, pp. 756-765. ISSN 0302-9743
Full text available as:
Official URL: http://dx.doi.org/10.1007/978-3-642-24965-5_85
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.
Export this item as:
To correct this item please ask your editor
Repository Staff Only: edit this item