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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

CyberShield : AI powered broken access control detection and mitigation

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pp. 1907–1917Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2283XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2283
Pages:
1907–1917

Abstract

Broken Access Control (BAC) is identified as the most dangerous and common vulnerability in the modern web systems. BAC can cause severe problems, such as data leaks, privilege escalations and unauthorized system manipulations. To address this issue, we propose to manually construct an extensive and well-featured dataset that can represent realistic traffic-based access control behaviors and behaviors of BAC attacks. This dataset serves as a preliminary resource for developing and evaluating machine learning algorithms capable of real-time BAC attack detection. The study has employed this dataset to develop various machine learning techniques such as Logistic Regression, Random Forest, Gradient Boosting, and XGBoost. Double is the contribution of the proposed work, namely the new BAC dataset that serves as a fundamental requirement for research communities, and a machine learning-based detection framework that is dedicated for real-time attacks detection. The Random Forest classifier achieved the highest overall accuracy (90.5%) and F1-score (0.8627), while XGBoost had the greatest AUC value of 0.9556. The system can also evolve and extended in future using the adaptive learning mechanisms, by adding more variants of attack to Produce a comprehensive set of attacks for a large dataset creation, implementing the detection framework to live environment for proactive prevention and automatic response in real time incidents.

Keywords

Broken access control (BAC)Access control vulnerabilitiesMachine learningData-driven securityPython-based security toolsDataset creation for cybersecurity

Subject Classifications

Primary 94A60Secondary 68T05

References

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