Dynamic Identity Management: A Key Component in Modern Cybersecurity Strategies

In today’s interconnected digital landscape, the need for secure and flexible identity management across multiple domains has become critical. Traditional Single Sign-On (SSO) mechanisms are heavily reliant on predefined trust relationships, which limits their effectiveness in dynamic and open environments where trust boundaries are constantly shifting. To address these challenges, this study introduces a novel approach to dynamic identity management by proposing a Dynamic Trust Identifying Mechanism that leverages advanced optimization techniques for secure cross-domain collaboration. The proposed solution is centred on the concept of a "Circle of Trust" (CoT) framework, which enables the sharing of identities and attributes across trust boundaries in accordance with predefined policies. Our approach aims to overcome the limitations of existing SSO mechanisms by dynamically managing trust relationships between entities across various domains. To achieve this, we introduce an emergent dynamic trust list that identifies trusted parties in real-time, allowing for adaptive and secure identity management. The core of our solution incorporates two advanced algorithms: the Pro-Wave Surf Optimization (PWSO) and the Sail Sea Fish Algorithm (SSFA). The Pro-Wave Surf Optimization is used to dynamically identify and evaluate trust relationships by simulating wave patterns, which helps in optimizing trust calculations across multiple domains. This algorithm enables the system to efficiently adjust trust relationships based on changes in the environment, making it suitable for managing identities in a dynamic, open ecosystem. Meanwhile, the Sail Sea Fish Algorithm is utilized to ensure secure data transfer between trusted parties by mimicking the behavior of fish swarming in the ocean. This algorithm enhances data security by optimizing the data transfer routes, reducing the risk of interception or unauthorized access during cross-domain interactions. Through this dual-algorithm approach, the proposed framework not only facilitates dynamic trust management but also provides a robust mechanism for secure data transfer across domains. This study details the methodology, implementation, and evaluation of this Dynamic Trust Identifying Mechanism, highlighting its potential to improve security and flexibility in cross-domain identity management. The results demonstrate that this approach can adaptively manage trust in open environments, offering a scalable and secure solution for modern identity management challenges.