Wang Bo, Wang Jiesheng, Li Mingchu
School of Applied Technology, University of Science and Technology Liaoning, Anshan 114051, China.
Julong Co., Ltd., Anshan 114051, China.
Sensors (Basel). 2025 Jun 30;25(13):4082. doi: 10.3390/s25134082.
With the rapid development of the Financial Internet of Things (FIoT), many intelligent devices have been deployed in various business scenarios. Due to the unique characteristics of these devices, they are highly vulnerable to malicious attacks, posing significant threats to the system's stability and security. Moreover, the limited resources available in the FIoT, combined with the extensive deployment of AI algorithms, can significantly reduce overall system availability. To address the challenge of resisting malicious behaviors and attacks in the FIoT, this paper proposes a trust-based collaborative smart device selection algorithm that integrates both subjective and objective trust mechanisms with dynamic blacklists and whitelists, leveraging domain knowledge and game theory. It is essential to evaluate real-time dynamic trust levels during system execution to accurately assess device trustworthiness. A dynamic blacklist and whitelist transformation mechanism is also proposed to capture the evolving behavior of collaborative service devices and update the lists accordingly. The proposed algorithm enhances the anti-attack capabilities of smart devices in the FIoT by combining adaptive trust evaluation with blacklist and whitelist strategies. It maintains a high task success rate in both single and complex attack scenarios. Furthermore, to address the challenge of resource allocation for trusted smart devices under constrained edge resources, a coalition game-based algorithm is proposed that considers both device activity and trust levels. Experimental results demonstrate that the proposed method significantly improves task success rates and resource allocation performance compared to existing approaches.
随着金融物联网（FIoT）的快速发展，许多智能设备已部署在各种业务场景中。由于这些设备的独特特性，它们极易受到恶意攻击，对系统的稳定性和安全性构成重大威胁。此外，FIoT中可用资源有限，再加上人工智能算法的广泛部署，会显著降低系统的整体可用性。为应对FIoT中抵御恶意行为和攻击的挑战，本文提出一种基于信任的协作智能设备选择算法，该算法将主观和客观信任机制与动态黑名单和白名单相结合，利用领域知识和博弈论。在系统执行过程中评估实时动态信任级别对于准确评估设备的可信度至关重要。还提出了一种动态黑名单和白名单转换机制，以捕捉协作服务设备不断变化的行为并相应地更新列表。所提出的算法通过将自适应信任评估与黑名单和白名单策略相结合，增强了FIoT中智能设备的抗攻击能力。在单攻击和复杂攻击场景下都保持了较高的任务成功率。此外，为应对边缘资源受限情况下可信智能设备的资源分配挑战，提出了一种基于联盟博弈的算法，该算法同时考虑了设备活动和信任级别。实验结果表明，与现有方法相比，所提出的方法显著提高了任务成功率和资源分配性能。
