AI-Driven Dynamic Access Control for IoT Devices at the Edge: A Trust-based Approach

The challenge of Internet of Things data management has grown considerably since recent times which make its security establishment especially problematic. The complex nature of IoT data security occurs because IoT environments feature diverse dynamic elements. The solution of these security challenges necessitates using sophisticated machine learning along with deep learning algorithms. Part of delivering IoT security consists of giving the correct authorization to devices that meet specified requirements. The proposed work presents a trust based dynamic access control protocol for IoT devices at the edge which uses AI-driven algorithms for operation. The system can monitor device conduct over time and current analysis to determine trust levels which then allows it to modify access permissions automatically for security improvement. Research performs an analysis of the proposed methodology to verify its ability to prevent unauthorized system entry while safeguarding data integrity in IoT networks. Several tests ran in an IoT emulation platform simulated different attack sequences that included invasive access attempts and device intrusions. The experimental testing showed that attack detection reliability increased substantially through the replacement of traditional static access control approaches. Under the trust based model attackers had better detection results because it proved more efficient at differentiating between both legitimate and malicious activities than traditional controls.