A Trust Evaluation Joint Active Detection Method in Video Sharing D2D Networks

The potentially malicious devices in D2D networks may spread forged videos to compromise system reliability. The prevailing passive trust model solutions have limitations, such as insufficient and inaccurate trust evidence, as well as weaker resistance to collusion attacks. This paper presents the T rust E valuation Joint A ctive D etection (TEAD) method, which employs content correctness as trust evidence and allows devices to verify the authenticity of received videos via the base station. TEAD incorporates an active detection method to proactively determine the trustworthiness of devices. This promotes interaction among devices, resulting in an increase in trust evidence and an improvement in the accuracy of evaluation. Moreover, TEAD introduces a trust calculation method with a penalty mechanism to strengthen the system's resilience against malicious attacks. Empirical results show that TEAD outperforms state-of-the-art methods by achieving a more accurate trust evaluation and faster trust convergence with low extra energy consumption.