Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13–14, 2019, Proceedings

Research Article

A Novel Wireless Sensor Networks Malicious Node Detection Method

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  • @INPROCEEDINGS{10.1007/978-3-030-21373-2_59,
        author={Hongyu Yang and Xugao Zhang and Fang Cheng},
        title={A Novel Wireless Sensor Networks Malicious Node Detection Method},
        proceedings={Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13--14, 2019, Proceedings},
        proceedings_a={SPNCE},
        year={2019},
        month={6},
        keywords={Wireless sensor network Network security Malicious node Reputation evaluation Cluster-head node},
        doi={10.1007/978-3-030-21373-2_59}
    }
    
  • Hongyu Yang
    Xugao Zhang
    Fang Cheng
    Year: 2019
    A Novel Wireless Sensor Networks Malicious Node Detection Method
    SPNCE
    Springer
    DOI: 10.1007/978-3-030-21373-2_59
Hongyu Yang1,*, Xugao Zhang1, Fang Cheng1
  • 1: Civil Aviation University of China
*Contact email: hyyang@cauc.edu.cn

Abstract

This paper proposed a malicious node detection model based on reputation with enhanced low energy adaptive clustering hierarchy (Enhanced LEACH) routing protocol (MNDREL). MNDREL is a novel algorithm, which is aimed at identifying malicious nodes in the wireless sensor network (WSN) more efficiently. Cluster-head nodes are first selected based on the enhanced LEACH routing protocol. Other nodes in WSN then form different clusters by selecting corresponding cluster-head nodes and determine the packets delivery paths. Each node then adds its node number and reputation evaluation value to the packet before sending it to the sink node. A list of suspicious nodes is then formed by comparing the node numbers, obtained through parsing with the packets by the sink node, with the source node numbers. To determine the malicious nodes in the network, the ratio of the suspect value to the trusted value of each node is further calculated and compared with a predefined threshold. The simulation experiments show that the proposed algorithm in this paper is more efficient in detecting malicious nodes in WSN with lower false alarm rate than other state-of-the-art methods.