MDTM: A Multi-dimensional Trust Management Scheme for Enhancing Security and Stability in SDN

An accurate and efficient trust evaluation mechanism is the cornerstone of maintaining the stability of SDN networks, especially in face of the escalating threats like Distributed Denial of Service (DDoS) attacks. The traditional trust evaluation mechanisms in SDN often lack of adaptability and accuracy, due to that they typically rely on the direct trust or ignore the influence of indirect and historical trust factors which lead to inaccurate trust evaluation and lower efficiency. In order to solve these problems, we propose a novel multidimensional trust evaluation mechanism consisting of three sub-modules: Device Behavior Trust Evaluation Scheme (DBTES), Machine Learning-based Trust Evaluation Scheme (MLTES), and Bayesian-Based Trust Evaluation Scheme (BBTES). These modules work together to enhance the precision and adaptability of trust assessments by capturing various aspects of device behavior. Additionally, we introduce a trust fusion algorithm that combines the Analytical Hierarchy Process(AHP) with Criteria Importance Through Inter-criteria Correlation (CRITIC) to optimize weight distribution, further improving the accuracy and robustness of trust evaluations. Our approach overcomes the limitations of existing methods by providing a more comprehensive and adaptable trust assessment. Simulation results show that under varying Malicious Device Ratio conditions, the MDTM method improves the task success rate by up to 4.39% compared to traditional methods, along with an increase in average trust values by 5.16%. Additionally, with changing historical factors, MDTM further improves trust values by 5.33%. These results demonstrate the enhanced resilience and effectiveness of our approach in maintaining network stability and security within SDN environments.