Abstract
In this paper, an effective method called HLR_DDoS is proposed to detect both low- and high-rate flooding attacks using a statistical approach. The method detects both types of attacks in two steps: (i) normal traffic analysis using cross-correlation measure and (ii) identification of suspicious high- and low-rate attack traffic using \(\alpha \)-divergence. The proposed method is evaluated on DDoS CAIDA 2007 and DARPA 2000 datasets.
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Hoque, N., Bhattacharyya, D.K. (2018). HLR_DDoS: A Low-Rate and High-Rate DDoS Attack Detection Method Using \(\alpha \)-Divergence. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_63
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DOI: https://doi.org/10.1007/978-981-10-6890-4_63
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