Hidden Markov Model for Shortest Paths Testing to Detect a Wormhole Attack in a Localized Wireless Sensor Network

The wormhole attack is one of the most popular and serious attacks in Wireless sensor networks and most proposed protocols to defend against this attack use extra hardware which impacts highly on the cost of implementation as well causing extra overheads which have high implications on the sensors power consumption. Due to the limited resources in the sensor nodes, protocols developed for wireless sensor networks should not impact heavily on the computational overheads and power consumption in order to extend the network lifetime. In this paper, we exploit the Hidden Markov Model (HMM) Viterbi algorithm, to detect the wormhole attack based on the maximum probabilities computed for a hidden state transition. We use different shortest paths hop count costs between a source and a destination node as the states input to the Viterbi algorithm, earmarking the least cost paths as the suspect wormhole paths, for a given observation sequence of the given shortest paths