An anomaly detection method based on feature mining for wireless sensor networks

To overcome the problems of large errors in data feature acquisition and long detection delays in traditional detection methods, this paper proposes an anomaly detection method based on feature mining for wireless sensor networks (WSNs). In our method, dimensionality reduction is performed on the data, all wireless sensor nodes are classified by a hybrid immune method, and data features are mined through vector set recognition. Moreover, the confidence interval is set by a time series, and the effective detection of abnormal data is conducted by comparison. The experimental results show that the maximum error of anomaly data collection is only 1.9%, the maximum time cost of anomaly detection is 8.4 s, and the P-R value is high, indicating that the proposed method is effective.