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 by utilizing such a cyclic tendency in anomaly detection. Our method differentiates suspicious traffic that has different statistical characteristics from normal traffic. At the same time, it learns about cyclic large- volume traffic, such as traffic for network operations, and finally considers it to be legitimate.