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Anomaly detection is the first step with a challenging task of securing a communication network, as the anomalies may indicate suspicious behaviors, attacks, network malfunctions, or failures. In this paper, we address the problem of not only detecting different anomalies, such as volume based (e.g., DDoS or Flash crowd) and spatial based (e.g., network scan), that arise simultaneously in the wild but also of attributing the anomalous point to a single-anomaly event causing it. Besides, we also tackle the problem of low-detection accuracy caused by the phenomenon of traffic drift. To this end, a novel adaptive profile-based anomaly detection scheme is proposed. More specifically, a more comprehensive metrics set is defined from the dimensions of temporal, spatial, category, and intensity to compose IP traffic behavior characteristic spectrum for fine-grained traffic characterization. Then, the digital signature matrix obtained by using the ant colony optimization (ACO) algorithm is applied to construct the baseline profile of the normal traffic behavior. Anomalous points are identified and analyzed by using confidence bands and a generic clustering technique, respectively. Finally, a lightweight updating strategy is applied to reduce the number of false positives. Real-world data of China Education Research Network backbone and synthetic data are collected to verify our proposal. The experimental results demonstrate that our approach provides a fine-grained behavior description ability and has significantly increased the detection accuracy compared with other similar alternatives.

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