Online anomaly detection for stream data has been explored recently, where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation. However, due to the inherent complex characteristics of stream data, such as quick generation, tremendous volume and dynamic evolution distribution, how to develop an effective online anomaly detection method is a challenge. The main objective of this paper is to propose an adaptive online anomaly detection method for stream data. This is achieved by combining isolation principle with online ensemble learning, which is then optimized by statistic histogram. Three main algorithms are developed, i.e., online detector building algorithm, anomaly detecting algorithm and adaptive detector updating algorithm. To evaluate our proposed method, four massive datasets from the UCI machine learning repository recorded from real events were adopted. Extensive simulations based on these datasets show that our method is effective and robust against different scenarios.
Specific Anomaly Detection Method in Wireless Communication Networks