Secure Convergence Model for Distributed Interactions

In modern digital ecosystems, distributed architectures such as cloud computing, multi-agent systems, and the Internet of Things (IoT) are facing challenges in ensuring a secure and reliable coordination of heterogeneous nodes in the modern digital ecosystem. The current solutions typically cover only one aspect of security, e.g., encryption, trust management, or anomaly detection, without being able to cover the whole spectrum of dynamic networks. This study introduces SCMDI to overcome such drawbacks and it is a secure convergence model for distributed interactions. SCMDI combines hybrid encryption, situational risk assessment, adaptive anomaly detection and policy based access control. It compares the reliability of the nodes with respect to past data and the actual behavior through machine learning models that observe network activity in real time. The framework guarantees confidentiality and integrity by using a combination of symmetric and asymmetric cryptography as well as automatically changing access policies based on the evolving network situations. As well as it is a low weight audit logging system offers tamper free traceability without adding excessive weight to computation. Simulation environment experiments with distributed environments show that SCMDI is effective in terms of detecting malicious activities, imposing trust ratings, and enforcing security policies. In general, the proposed model can provide a high performance, high scalability and intelligent yet secure and collaborative protection to distributed computing environments.