Anomaly Detection Algorithms for Streaming Data: Performance Comparison

Today’s most of the data are streaming time-series data, where is very important anomaly detection over this data because gives significant information of possible critical situations. Detecting anomalies in big streaming data is yet difficult task because we have to process them in real time, even before they are stored and instantly alarm on potential threats. For real time streaming data is important the algorithm used for anomaly detection to be robust with low processing time, eventually at the cost of the accuracy. The aim of this paper is to measure the performance of such algorithms and them to compare with our previously proposed algorithm HW-GA with other existing methods as ARIMA, Moving Average and Holt Winters. The algorithms are implemented in R system and tested 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). As a result of this research we may say that our algorithm HW-GA outperforms others algorithm that we have compared by showing less CPU usage and execution time. Our continues interest is to monitor 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.