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Flooding attacks detection in backbone traffic using power divergence

Content Provider	ACM Digital Library
Author	Makke, Ali Mehaoua, Ahmed Salem, Osman Moungla, Hassine Assaad, Mohamad
Abstract	Flooding attacks detection in traffic of backbone networks requires generally the analysis of a huge amount of data with high accuracy and low complexity. In this paper, we propose a new scheme to detect flooding attacks in high speed networks. The proposed mechanism is based on the application of Power Divergence measures over Sketch data structure. Sketch is used for random aggregation of traffic, and Power Divergence is applied to detect deviations between current and established probability distributions of network traffic. We focus on tuning the parameter of Power Divergence to optimize the performance. We evaluate our approach using real Internet traffic traces, obtained from MAWI trans-Pacific wide transit link between USA and Japan. Our results show that the proposed approach outperforms existing solutions in terms of detection accuracy and false alarm ratio.
Starting Page	15
Ending Page	20
Page Count	6
File Format	PDF
ISBN	9781450316262
DOI	10.1145/2387191.2387195
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hellinger distance Chi-square divergence Kullback-leibler divergence Power divergence Sketch Anomaly detection
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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