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We present a method of detecting network anomalies, such as DDoS (distributed denial of service) attacks and flash crowds, automatically in real time. We evaluated this method using measured traffic data and found that it successfully differentiated suspicious traffic. In this paper, we focus on cyclic traffic, which has a daily and/or weekly cycle, and show that the differentiation accuracy is improved...
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