Machine learning in identity and access management systems: Survey and deep dive

Aboukadri, Sara; Ouaddah, Aafaf; Mezrioui, Abdellatif

doi:10.1016/j.cose.2024.103729

Cited by 16 publications

(6 citation statements)

References 80 publications

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“…The more substantial effect observed in larger organizations and specific industries (technology and finance) highlights the contextual factors that can amplify or attenuate the effectiveness of RBAC in insider threat mitigation [61][62]. This insight is particularly relevant, considering the diverse operational environments and threat profiles across sectors, emphasizing the need for tailored RBAC strategies that account for organizational and industry-specific variables [13] [35].…”

Section: Discussionmentioning

confidence: 99%

“…The evolving nature of insider threats requires that RBAC systems be continuously updated and refined [34]. The integration of advanced technologies, such as machine learning and artificial intelligence, is seen as a promising direction for making RBAC more adaptive and effective against sophisticated insider threats [13] [35] [36].…”

Section: Rbac's Role In Mitigating Insider Threatsmentioning

confidence: 99%

“…While RBAC excels in defining access based on organizational roles, it faces limitations in fully addressing the multifaceted nature of insider threats. One significant limitation is its static nature, as traditional RBAC systems are not inherently designed to adapt to access requirements' temporal or contextual nuances [35]. For example, an employee's access needs may vary depending on the time of day, location, or specific project involvement, factors that static RBAC roles may not accommodate.…”

Section: Challenges and Limitations Of Rbac In Insider Threat Mitigationmentioning

confidence: 99%

“…Moreover, addressing these challenges and constraints requires a multifaceted approach, combining technical solutions with organizational strategies. Enhancements such as dynamic RBAC, which adapts to real-time changes in the organization, and integrating RBAC with behavior analytics and other security measures can provide more comprehensive protection against insider threats [35] [13]. Additionally, ongoing management, regular audits of roles and permissions, and stakeholder involvement in defining and refining roles are essential to maintaining an effective RBAC system.…”

Section: Recentmentioning

confidence: 99%

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From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Marquis

2024

JERR

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This study investigates the effectiveness of Role-Based Access Control (RBAC) systems in mitigating insider threats to database security within various organizational environments. Insider threats represent a significant challenge for database security, necessitating robust and adaptive security measures. By delineating access based on users' roles within an organization, RBAC emerges as a critical tool against such threats. Employing a quantitative research methodology, this work gathered data through a survey targeting professionals directly involved in the security and management of organizational databases across technology, finance, healthcare, and government industries. The study utilized Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the measurement model and analyze the relationships between RBAC effectiveness, implementation challenges, RBAC enhancements, and their collective impact on insider threat reduction. Findings indicate that RBAC effectively reduces unauthorized access and data breaches, significantly mitigating insider threats. However, implementation challenges such as role definition complexity and adapting to dynamic access needs emerge as notable obstacles. Enhancements in RBAC, mainly through integrating technologies like machine learning and dynamic access controls, are identified as critical mediators that enhance RBAC's effectiveness. The study concludes that while RBAC is a vital tool for database security, its success depends on continuous improvement and customization to specific organizational contexts. It recommends developing continuous enhancement programs for RBAC systems, specialized training for administrators, and the customization of RBAC strategies to meet unique organizational and industry needs. These measures are crucial for optimizing RBAC's effectiveness against insider threats.

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Section: Discussionmentioning

confidence: 99%

Section: Rbac's Role In Mitigating Insider Threatsmentioning

confidence: 99%

Section: Challenges and Limitations Of Rbac In Insider Threat Mitigationmentioning

confidence: 99%

Section: Recentmentioning

confidence: 99%

See 2 more Smart Citations

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Marquis

2024

JERR

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“…Undoubtedly, the IoT is considered a significant prospect that contributes significantly to advanced digital technology. However, currently, the risk of unauthorized access due to the connection of a large number of devices over the network is one of the critical challenges [13,14], along with IoT analytics and the IoT process hierarchy.…”

mentioning

confidence: 99%

Special Issue on Information Security and Cryptography: The Role of Advanced Digital Technology

Khan,

Por

2024

Applied Sciences

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Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches

Ahmed,

Syed,

Maaruf

et al. 2024

Computing

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Machine learning in identity and access management systems: Survey and deep dive

Cited by 16 publications

References 80 publications

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

From Theory to Practice: Implementing Effective Role-Based Access Control Strategies to Mitigate Insider Risks in Diverse Organizational Contexts

Special Issue on Information Security and Cryptography: The Role of Advanced Digital Technology

Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches

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