Detecting Sybil nodes in stationary wireless sensor networks using learning automaton and client puzzles

A well-known harmful attack against wireless sensor networks (WSNs) is the Sybil attack. In a Sybil attack, WSN is destabilised by a malicious node which forges a large number of fake identities to disrupt network protocols such as routing, data aggregation, and fair resource allocation. In this study, the authors suggest a new algorithm based on a composition of learning automaton (LA) model and client puzzles theory to identify Sybil nodes in stationary WSNs. In the proposed algorithm, each node sends puzzles to its neighbours periodically during the network lifetime and tries to identify Sybil nodes among them, considering their response time (puzzle solving time). In this algorithm, each node equipped with a LA to reduce the communication and computation overhead of sending and solving puzzles. The proposed algorithm has been simulated using J-SIM simulator and simulation results have shown that the proposed algorithm can detect 100% of Sybil nodes and the false detection rate is about 5% on average. Also, the performance of the proposed algorithm has been compared to a wellknown neighbour-based algorithm through experiments and the results have shown that the proposed algorithm is significantly better than this algorithm in terms of detection and false detection rates.