Citation :

Rashmin Prajapati, Sweta S. Panchal, Neha Soni, Sandipkumar R. Panchal, "A Risk-Adaptive Attribute-Based Access Control Framework with Ternary Decision Logic for Context-Aware IoT Security," International Journal of Electronics and Communication Engineering, vol. 13, no. 9, pp. 120-146, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I9P108

Abstract

As the Internet of Things (IoT) grows rapidly, security concerns are rising, as these devices are diverse, the operating environments change constantly and data are exchanged continuously. This paper is an extension of our previous published machine-learning based Risk-Adaptive Attribute-Based Access Control (RAd-ABAC) system where contextual risk estimation and Permit–Review–Deny authorization were introduced. The present study builds upon this in an implementation-oriented architecture including a Contextual Attribute Collector, Risk Evaluation Engine, and Decision Management Engine. These attributes of the user (role, location, time, device) are converted to a normalized risk indicator and weighted through a mechanism of risk aggregation. The contextual risk score is then fed into two decision thresholds to ultimately determine access rules (Permit, Review, Deny). Three publicly available IoT security datasets—IoT-23, TON_IoT, and IoT Intrusion Detection dataset—were used to evaluate the framework. These datasets were used to map network, temporal, behavioral and device attributes to access-control attributes. The models were built and tested with a 70:30 train–test split. Logistic Regression and Random Forest models were trained and tested with a 70:30 training-testing split. The accuracy, precision, recall, F1 score, confusion matrix, receiver operating characteristic curve, area under the curve and false-positive and false-negative rates were measured. The Random Forest model outperformed Logistic Regression with precision and recall values closer to each other, with an accuracy of over 96%. The intermediate Review state allowed indefinite access requests to be reviewed again before granting or denying access, which helped to decrease false dichotomy decisions. The present study introduces a modular processing architecture, explicit contextual-attribute transformation, quantitative validation of the processing across three heterogeneous datasets, and expanded. These developments provide greater visibility, flexibility and utility of context-aware access control in diverse IoT environments.

Keywords

Attribute-Based Access Control (ABAC), Context-Aware Access Control, Internet of Things (IoT) Security, Machine Learning in Security, Risk-Adaptive Access Control (RAdAC), Ternary Decision Logic.

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