Real Time Adaptive Access Control with Behavioral Analytics for Enhanced Cybersecurity in IoT and Cloud Systems

DACS dynamically adjusts access permissions by analyzing user behavior, context, and risk in real time.It evaluates activity logs, device details, and network conditions to identify anomalies, such as irregular login times or unfamiliar devices, triggering access restrictions or additional authentication.Using a neural network trained on historical data, DACS assigns risk scores to access attempts, categorizing them as low, moderate, or high-risk.Low-risk behaviors allow seamless access, while high-risk attempts undergo scrutiny.Our implementation demonstrates DACS's scalability, low latency, and superior detection accuracy compared to static models.These findings position DACS as a proactive, intelligent solution to address the dynamic challenges of secure access in real-time, high-demand environments.