Fuzzy Adaptive Threshold Determining in the Key Inheritance Based Sensor Networks

Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks. False data injection attacks will not only cause false alarms that waste real world response efforts, but also drain the finite amount of energy in a battery powered network. The key inheritance based filtering scheme can detect a false report at the very next node of the compromised node that injected the false report before it consumes a significant amount of energy. The choice of a security threshold value in this scheme represents a trade off between security and overhead. In this paper, we propose a fuzzy adaptive threshold determining method for the key inheritance based filtering scheme. The fuzzy rule based system is exploited to determine the security threshold value by considering the average energy level of all the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes. We also introduce a modified version of this scheme to reduce the overhead for changing the threshold value. The proposed method can conserve energy, while it provides sufficient resilience.