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DDoS Detection Algorithm Based on Preprocessing Network Traffic Predicted Method and Chaos Theory
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AI
DOI：10.1109/LCOMM.2013.031913.130066.png)
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Abstract
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Distributed denial-of-service (DDoS) flooding attacks still pose great threats to the Internet even though various approaches and systems have been proposed. In this paper, we firstly pre-process network traffic by cumulatively averaging it with a time range, and using the simple linear AR model, and then generate the prediction of network traffic. Secondly, assuming the prediction error behaves chaotically, we use chaos theory to analyze it and then propose a novel network anomaly detection algorithm (NADA) to detect the abnormal traffic. With this abnormal traffic, we lastly train a neural network to detect DDoS attacks. Our preliminary experiments and analyses indicate that our proposed DDoS detection algorithm can accurately and effectively detect DDoS attacks.
Keywords:
Distributed denial-of-service (DDoS)
anomaly detection
AR model
chaotic
Journal
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4.4
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1.3W
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