Adaptive Attribute-based Encryption(A-ABE) Framework for Securing Smart IoT Networks

In this paper, we proposed and implemented an Adaptive Attribute-Based Encryption (A-ABE) Framework that combines Ciphertext-Policy ABE with context-aware machine learning to secure smart IoT networks. The framework focused on key limitations of traditional ABE schemes, and their inability for adapting to dynamic user contexts and access requirements in real time scenarios. By deploying lightweight machine learning models, the system can be enabled to intelligently analyze contextual data like location, user behavior, and role changes—and update access policies accordingly, without requiring manual re-encryption or administrative intervention. The implementation and evaluation conducted in a simulated smart healthcare environment demonstrated that the A-ABE framework offers an optimal balance between security, adaptability, and system efficiency. The experimental results showed low encryption and decryption delays, high policy enforcement accuracy , and modest increases in resource utilization, making the solution is viable for deployment on edge devices. Overall, the proposed framework improves both the confidentiality of sensitive IoT data and the resilience of access control mechanisms against insider threats, contextual anomalies, and unauthorized access. This research confirms that combining ABE with real-time machine learning provides a scalable and intelligent approach to enforcing secure access in dynamic IoT environments.