Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

DOI: http://dx.doi.org/10.15439/2024R75

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 151–155 (2024)

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Abstract. 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.

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