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

  • Author(s): Mojtaba Jamshidi 1 ; Mehdi Esnaashari 2 ; Aso Mohammad Darwesh 3 ; Mohammad Reza Meybodi 4
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  • Source: Volume 13, Issue 13, 13 August 2019, p. 1988 – 1997
    DOI:  10.1049/iet-com.2018.6036 , Print ISSN 1751-8628, Online ISSN 1751-8636
© The Institution of Engineering and Technology
Received 19/10/2018, Accepted 13/05/2019, Revised 25/04/2019, Published 15/05/2019

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.

Inspec keywords: computer network security; telecommunication security; protocols; security of data; resource allocation; wireless sensor networks

Other keywords: malicious node; Sybil attack; Sybil nodes; network protocols; puzzle solving time; network lifetime; learning automaton; neighbour-based algorithm; stationary wireless sensor networks; false detection rate; client puzzles theory; harmful attack; sending solving puzzles

Subjects: Data security; Wireless sensor networks; Protocols; Computer communications

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