  With the tremendous advances of the wireless devices technology, securing
wireless sensor networks became more and more a vital but also a challenging
task. In this paper we propose an integrated strategy that is meant to discover
malicious nodes within a sensor network and to expel them from the network
using a node self-destruction procedure. Basically, we will compare every
sensor reading with its estimated values provided by two predictors: an
autoregressive predictor [1] that uses past values provided by the sensor under
investigation and a neural predictor that uses past values provided by adjacent
nodes. In case the absolute difference between the measured and the estimated
values are greater then a chosen threshold, the sensor node becomes suspicious
and a decision block is activated. If this block decides that the node is
malicious, a self-destruction procedure will be started.
