TY - GEN
T1 - Predictive Enhancement of RBAC Policies Using Access Log Analytics
T2 - 9th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2025
AU - Amour, Shmuel
AU - Gudes, Ehud
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/1/1
Y1 - 2026/1/1
N2 - Access control is vital for protecting organizational resources, with Role-based access control (RBAC) offering a widely adopted framework for managing permissions. However, static role assignments in traditional RBAC systems often become misaligned with evolving organizational structures and user behaviors, leading to inefficiencies and potential security risks. This paper explores a predictive enhancement to RBAC policies by analyzing historical access logs using Hierarchical Clustering (HCL) techniques. The proposed approach uncovers behavioral access patterns to support data-driven refinement of role assignments. By incorporating behavioral clustering into access control workflows, the method helps align permissions with observed usage trends and may reduce excessive privilege assignments. Evaluation on a real-world dataset demonstrates that the model adapts roles based on access behavior, offering a step toward more responsive and behavior-aware access governance.
AB - Access control is vital for protecting organizational resources, with Role-based access control (RBAC) offering a widely adopted framework for managing permissions. However, static role assignments in traditional RBAC systems often become misaligned with evolving organizational structures and user behaviors, leading to inefficiencies and potential security risks. This paper explores a predictive enhancement to RBAC policies by analyzing historical access logs using Hierarchical Clustering (HCL) techniques. The proposed approach uncovers behavioral access patterns to support data-driven refinement of role assignments. By incorporating behavioral clustering into access control workflows, the method helps align permissions with observed usage trends and may reduce excessive privilege assignments. Evaluation on a real-world dataset demonstrates that the model adapts roles based on access behavior, offering a step toward more responsive and behavior-aware access governance.
KW - Access Log Analytics
KW - Hierarchical Clustering
KW - Machine Learning
KW - Predictive Access Control
KW - Role-Based Access Control (RBAC)
UR - https://www.scopus.com/pages/publications/105023392381
U2 - 10.1007/978-3-032-10759-6_21
DO - 10.1007/978-3-032-10759-6_21
M3 - Conference contribution
AN - SCOPUS:105023392381
SN - 9783032107589
T3 - Lecture Notes in Computer Science
SP - 314
EP - 325
BT - Cyber Security, Cryptology, and Machine Learning - 9th International Symposium, CSCML 2025, Proceedings
A2 - Akavia, Adi
A2 - Dolev, Shlomi
A2 - Lysyanskaya, Anna
A2 - Puzis, Rami
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 December 2025 through 5 December 2025
ER -