DTMS: A Dual Trust-Based Multi-level Sybil Attack Detection Approach in WSNs.

Sensor networks have emerged as a promising technology for collecting data and capturing information about the physical world. However, these networks are often deployed in harsh and inaccessible environments, making them susceptible to various security attacks. One of the most severe attacks in wireless sensor networks (WSNs) is the Sybil attack, where a malicious node illegitimately assumes multiple fraudulent identities to deceive and disrupt the network. To address this complex and challenging problem, this paper proposes a dual trust-based multi-level Sybil (DTMS) attack detection approach for WSNs. The approach employs a multi-level detection system that verifies the identity and location of each node. At each level (Cluster Member, Cluster Head, and Base Station), a trust value is calculated based on the node's behavior. The trust value incorporates both communication trust and data trust, ensuring a satisfactory level of trust before accepting information from a node. The DTMS approach incorporates a dynamic reward and penalty coefficient in the trust function to accurately capture the severity of a node's behavior. Additionally, data aggregation techniques are employed to reduce communication overhead and conserve energy. The performance of DTMS is evaluated based on various metrics such as the severity of the trust function, true detection rate, false detection rate, residual energy, network lifetime, and packet loss ratio. Simulation results demonstrate that DTMS can effectively detect Sybil nodes, achieving a 100% detection rate in a malicious environment. Furthermore, a comparison with existing schemes highlights the desirable performance of DTMS across multiple parameters, including true detection rate, false detection rate, energy consumption, packet loss rate, and the number of active nodes. [ABSTRACT FROM AUTHOR]