Histogram-Based Online Anomaly Detection in Hierarchical Wireless Sensor Networks

Online anomaly detection is critical for protecting wireless sensor networks (WSNs) from cyber-attacks and random faults, which handles the streaming data in real-time. Comparing to other techniques, histogram-based anomaly detection is cheaper in computation, which should be suitable for WSNs. However, performing histogram-based anomaly detection with an online manner in WSNs is not a straightforward issue. Most of the existing histogram-based schemes have to depend on a verification procedure, which costs a great amount of computational overhead as well as communication overhead. Thus, it almost wipes out the advantage of low complexity of histogram-based anomaly detection. This paper introduces a simple estimating approach to detect anomalies with the histogram, which takes account into the distributed manner and online manner at the same time. It also proves the error caused by the new estimate is very small, through a theoretical analysis. Moreover, the optimal parameter will be suggested by minimizing the error. Finally, a set of experiments are implemented with a real WSN dataset, which prove the new scheme is effective and efficient.