Abstract
Most of the today's world data are streaming, time-series data, where anomalies detection gives significant information of possible critical situations. Yet, detecting anomalies in big streaming data is a difficult task, requiring detectors to acquire and process data in a real-time, as they occur, even before they are stored and instantly alarm on potential threats. Suitable to the need for real-time alarm and unsupervised procedures for massive streaming data anomaly detection, algorithms have to be robust, with low processing time, eventually at the cost of the accuracy. In this work we compare the performance of our proposed anomaly detection algorithm HW-GA[1] with other existing methods as ARIMA [10], Moving Average [11] and Holt Winters [12]. The algorithms are tested and results are visualized in the system R, on the three Numenta datasets, with known anomalies and own e-dnevnik dataset with unknown anomalies. Evaluation is done by comparing achieved results (the algorithm execution time and CPU usage). Our interest is monitoring of the streaming log data that are generating in the national educational network (e-dnevnik) that acquires a massive number of online queries and to detect anomalies in order to scale up performance, prevent network downs, alarm on possible attacks and similar.
AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment