A Hough-transform-based anomaly detector with an adaptive time interval

Abstract

Internet traffic anomalies are a serious problem that compromises the availability of optimal network resources. Numerous anomaly detectors have recently been proposed, but maintaining their parameters optimally tuned is a difficult task that discredits their effectiveness for daily usage. This article proposes a new anomaly detection method based on pattern recognition and investigates the relationship between its parameter set and the traffic characteristics. This analysis highlights that constantly achieving a high detection rate requires continuous adjustments to the parameters according to the traffic fluctuations. Therefore, an adaptive time interval mechanism is proposed to enhance the robustness of the detection method to traffic variations. This adaptive anomaly detection method is evaluated by comparing it to three other anomaly detectors using four years of real backbone traffic. The evaluation reveals that the proposed adaptive detection method outperforms the other methods in terms of the true positive and false positive rate.