Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method

Abstract

Data anomaly detection in wireless sensor networks, which is one of the important technologies and study areas, is a method that enhances data quality and data reliability. Besides data enhancing methods such as estimating missing data, deduplication, noise removal; anomaly detection is important in terms of finding data patterns which are out of normal data. This stage influences next analysis and decision processes and plays an important role in determining events, faults or unexcepted but meaningful patterns. This study proposes the Histogram Based Outlier Score (HBOS) method to detect anomalies in data acquired by wireless sensor networks. In respect to anomaly detection methods used in this area, such as data classification, data clustering, statistical, distance based and support vector machines based approaches, histogram based algorithms are unsupervised and provide fast solutions.