Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis

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

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.

Citation

Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2011). Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis. In Neural Information Processing; Lecture Notes in Computer Science (756-765). Springer. https://doi.org/10.1007/978-3-642-24965-5_85

• Publication Date: 2011
• Deposit Date: Jun 16, 2017
• Electronic ISSN: 1611-3349
• Publisher: Springer
• Pages: 756-765
• Book Title: Neural Information Processing; Lecture Notes in Computer Science
• Chapter Number: Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis
• ISBN: 9783642249648; 9783642249655
• DOI: https://doi.org/10.1007/978-3-642-24965-5_85
• Keywords: Denial-of-Service Attack, Euclidean Distance Map, Multivariate Correlations, Anomaly Detection
• Public URL: http://researchrepository.napier.ac.uk/Output/948462