Memory Efficient Protocols for Detecting Node replication attacks in wireless sensor networks

Abstract

Sensor networks deployed in hostile areas are subject to node replication attacks, in which an adversary compromises a few sensors, extracts the security keys, and clones them in a large number of replicas, which are introduced into the network to perform insider attacks. Memory overhead, energy efficiency and detection probability are the main technical concerns for any replication detection protocol. The previous distributed solutions either require network-wide spontaneous change of pseudo-random numbers or incur significant memory and energy overhead to the sensors, especially in the central area of the deployment. In this paper, we propose four replication detection protocols that have high detection probability, low memory requirement, and balanced energy consumption. The new protocols use Bloom filters to compress the information stored at the sensors, and use two new techniques, called cell forwarding and cross forwarding, to improve detection probability, further reduce memory consumption, and in the mean time distribute the memory and energy overhead evenly across the whole network. Simulations show that the protocols can achieve nearly 100% detection probability with average memory reduction up to 91%.