An Adaptive Immune Based Anomaly Detection Algorithm For Smart WSN Deployments

The growing attention in smart WSN deployments for monitoring, security and optimization applications urges the design of new tools in order to recognize, as soon as a possible, anomalous states of systems whenever they occur. In order to develop an anomaly detection system enabling to discover unusual events in a non-stationary process, a scalable immune based strategy has been adopted. The algorithm works as an instance based 1-class classifier capable to un-supervisedly model the "normal" spatial-temporal variable behavior of the system identifying first order anomalies. Typical immune-like processes guarantee a slow adaptation of the set of local patterns to long term variation in the monitored system. The algorithm has been applied to a several real scenarios showing to be able to work on both on resource constrained WSN nodes and on dealing with large data streams in centralized data processing facilities.