مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 14 55 2026 11 9 Neural Risk-Adaptive Access Control: A Behavior-Context Hybrid Framework for Adaptive Authorization in Dynamic Environments Neural Risk-Adaptive Access Control: A Behavior-Context Hybrid Framework for Adaptive Authorization in Dynamic Environments 196 213 en Donya Piriaei Department of Computer Engineering, Bo.C., Islamic Azad University, Borujerd, Iran Afshin Rezakhani Ayatollah Boroujerdi University Hamid Haj Seyyed Javadi Shahed University Leila Rikhtechi Department of Computer Engineering, Bo.C., Islamic Azad University, Borujerd, Iran 2026 6 8 <p>We present a risk-adaptive access control framework that integrates dynamic behavioral modeling with Attribute-Based Access Control (ABAC) to enable real-time decision-making in volatile and high-risk environments. The proposed system introduces two novel components: the Attention-based Temporal Unit (ATU) for capturing sequential access behaviors, and the Contextual Scoring Module (CSM) for quantifying contextual and environmental signals. Risk is estimated via a multi-dimensional function that combines behavioral patterns, device and location attributes, and real-time threat indicators. An adaptive decision threshold, continuously refined based on false-accept and false-reject outcomes, ensures a calibrated balance between security and operational usability. Computed risk scores are seamlessly embedded into ABAC policies to support fine-grained enforcement. Evaluation on four domain-relevant datasets&mdash;Login Data Set for Risk-Based Authentication (RBA), CERT Insider Threat Test Dataset, CIC-IDS2017, and RT-IoT2022&mdash;used to emulate access control scenarios, shows F1-scores up to 0.95 and precision up to 0.94, outperforming strong baselines by up to 12%. The results demonstrate robustness across authentication, insider threat, intrusion, and IoT domains, supporting the framework&rsquo;s applicability in real-time, Risk-Adaptive access systems for wireless communication and IoT environments, particularly in emerging 5G/6G contexts. The modular architecture also separates behavioral, contextual, and environmental evidence so that each source can contribute to the final authorization decision without requiring a single static risk signal.</p> http://jist.ir/ar/Article/Download/53783