IEEE Communications Letters · 2013 · 69 citations · 5 references
ComputingCybersecurityInformation TechnologyForecastingMachine LearningNetwork SecurityAnomaly Detection In Time SeriesChaos TheoryDenial-of-service AttacksIntrusion DetectionNetwork Traffic AnalysisThreat DetectionDdos DetectionDenial-of-service DetectionDistributed Denial Of ServiceFloodingFlooding AttacksNetwork Anomaly DetectionNetwork Traffic Anomaly DetectionNetwork Traffic Modeling
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 eechaoticallyee, 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.
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Alan Wolf, J. B. Swift, Harry L. Swinney et al. · Physica D Nonlinear Phenomena · 1985 · 9.4K citations · Full text
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