Jul 2026· World Journal of Advanced Research and Reviews· Vol 31, pp. 868-882· 0 citations
TL;DR
A behavioral risk-aware access control framework that unifies four mechanisms within a single architecture that discusses how the framework's design compares with role/attribute-based access control, single-party legacy-contact tools, and blockchain and ML-based alternatives, and reports the scale limitations of the present evaluation transparently.
Abstract
Digital legacy systems must decide, without the account owner present to confirm intent, whether a request for emergency access to sensitive testamentary documents is legitimate. Existing approaches address this problem only partially: platform-specific legacy-contact features are not interoperable and require no independent corroboration, attribute- and role-based access control models regulate access during active use but are not designed for posthumous or inactivity-triggered release, and machine-learning-based anomaly detection improves suspicious-activity detection at the cost of data and computational requirements that are impractical for a lightweight, single-tenant application. This paper proposes and evaluates a behavioral risk-aware access control framework that unifies four mechanisms within a single architecture: (i) a rule-based behavioral risk-scoring engine that continuously derives a 0-100 risk value from failed-login and high-risk-action signals; (ii) a three-stage, checkpoint-based inactivity/liveness-detection algorithm that escalates from confirmation e-mails to an automatic emergency-access trigger; (iii) a threshold (2-of-3) multi-party authorization protocol that releases access only once independent trusted contacts submit single-use cryptographically random codes; and (iv) role-based access control combined with AES-256-CBC document encryption and full audit logging. We instantiate the framework in a working prototype, LegacyVault, built on Node.js, Express.js, and MySQL, and evaluate it through functional, performance, and security testing aligned to an explicit threat model covering credential compromise, trusted-contact collusion, and insider access. Testing shows that all implemented controls (authentication, encryption, threshold approval, inactivity escalation, and audit logging) operate as designed, with core operations completing in 1.2-7.0 seconds on a local test deployment. We discuss how the framework's design compares with role/attribute-based access control, single-party legacy-contact tools, and blockchain and ML-based alternatives, and we report the scale limitations of the present evaluation transparently rather than overstating a small, single-environment test as a production-grade security guarantee.
The results demonstrate the feasibility of organizing heterogeneous runtime evidence and establishing an executable authorization-risk decision and coordinated-response loop without modifying application code, but do not establish general applicability in production environments.
Si-Chen Lin, Yuchao Luo, Zihan Zhu et al.· 0 citations
: With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity– based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the proposed method can achieve an AUC of approximately 0.927, orange demonstrating effective discrimination between normal and malicious behavioral patterns. Furthermore, the framework maintains a low False Restriction Rate of around 1.605% while still detecting attacks at a meaningful level, thereby achieving a balance between security and usability. This study highlights the feasibility of a risk-adaptive digital identity protection framework that dynamically evaluates digital identity and adaptively responds based on risk levels in IoMT environments.
Joo-Hyun Park, Jiho Choi, Libor Mesícek et al.· Computers, Materials & C...· 0 citations
LegacyVault is a web-based digital will management system that integrates authentication with password hashing, AES document encryption, rule-based behavioral risk scoring, inactivity detection, and comprehensive audit logging within a single lightweight system.
J. A. Hampo, Kinyuy Marie-Noel Ngala, Fomukom Mark Nsah Tanyi et al.· International Journal Of Eng...· 0 citations
SafeGuard is presented, a lightweight three-tier client-server architecture for real-time endpoint monitoring, threat reporting, and administrative response that demonstrates that an open-source technology stack can provide real-time endpoint visibility and coordinated administrative response without commercial licensing costs.
Gideon Francis Oghie, DivineDavid Shittu Abolanle· 0 citations
The PAM in z/OS systems needs additional measures in the realm of protecting sensitive data from misuse by insiders and credential abuse. Classic approaches using authentication through static mechanisms do not provide adequate protection of administrative sessions that use RACF, TSO, and SDSF, thus leaving systems open to risks associated with administrative activities. To combat these risks, this paper proposes implementing a new control approach for PAM known as the Zero-Trust Control Framework, based on behavioural analysis, graph theory, and entropy calculations. The new tool developed as part of this framework is called Behavioural Identity Drift Analyzer (BIDA). As such, the Privilege Interaction Graph Transformer (PIGT) leverages graph neural networks and transformers to generate privilege interaction graphs that detect the escalation routes, dependencies, and propagation vectors of insider threats in near real-time. Lastly, the Entropy Adaptive Zero-Trust Orchestrator (EAZTO) analyzes uncertainties in user actions, commands, and privileges in order to apply adaptive control measures such as multi-factor authentication, privilege reduction, or even session termination. In combination, these four modules provide layers of protection and achieve continuous PAM decision-making at 90 percent accuracy for the purpose of enhancing zero trust enforcement in z/OS administration.
R. Shaw· Journal of Intelligent Decis...· 0 citations
Role-Based Access Control (RBAC) is widely used in web applications to regulate user permissions and resource access. Although effective at authorization, RBAC does not evaluate whether the behavior within an authenticated session is legitimate. As a result, threats such as credential misuse, insider attacks, and session hijacking may continue undetected even when requests satisfy existing access policies.To address this limitation, this paper presents RA-SFAD (Role-Aware Session Fingerprinting and Anomaly Detection), a lightweight framework for identifying abnormal user activity in RBAC-enabled environments. Instead of relying on a single behavioral model for all users, the framework constructs separate behavioral fingerprints for individual RBAC roles and monitors deviations from role-specific usage patterns. The detection pipeline combines per-role Isolation Forest models for identifying statistical anomalies with an LSTM-based sequence model that analyzes the temporal flow of user actions. In addition, SHAP (SHapley Additive exPlanations) is incorporated into the alerting mechanism to provide interpretable feature-level explanations for detected anomalies.The proposed framework was evaluated on a controlled dataset containing 5,800 labeled sessions distributed across three user roles. Experimental results show that RA-SFAD achieves 94% detection accuracy with an F1-score of 0.92 while maintaining a false positive rate of 4%, outperforming role-agnostic baseline approaches. The framework records an average detection latency of 420 ms and supports a throughput of approximately 2,400 sessions per second, indicating that it can operate efficiently on standard application server infrastructure. An ablation analysis further demonstrates that the role-aware modeling, hybrid detection strategy, and explainability module each contribute to the overall system performance.
H. K· International Conference Com...· 0 citations
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