A lightweight and anti‐collusion trust model combined with nodes dynamic relevance for the power internet of things

Abstract

Abstract A large number of monitoring sensors are introduced in the power grid. However, the traditional trust models commonly used for edge‐side security management are weak in detecting large‐scale malicious interactions and collusion attacks. For that, a lightweight and anti‐collusion trust model combined with nodes’ dynamic relevance for the power Internet of Things (IoT) is proposed. Firstly, a global trust management system is constructed according to the working mechanism of sensors in the power grid. After that, trust feedback and contact frequency of the devices are combined to build an adaptive dynamic weight vector based on relevance volatility. Fluctuations in trust values are reduced and the trust difference between normal and malicious nodes is widened. An anti‐collusion algorithm based on contact set awareness is also designed to effectively detect collusion attacks. The checksum local broadcast is established in the trust model to counteract the risk of intelligent terminal failure. The results show that the trust model achieves 100% accuracy of node discrimination when the maximum proportion of malicious nodes is 20% in a 50‐node network scale. In addition, the calculation time of the overall model is 211 ms and the memory consumption is 161 kb, which is suitable for power IoT sensor networks.

Keywords

• anti‐collusion
• dynamic relevance analysis
• edge computing
• global trust feedback
• lightweight
• trust model