Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Identity and Access Management Field: Challenges and State of the Art

Aboukadri, Sara; Ouaddah, Aafaf; Mezrioui, Abdellatif

doi:10.1007/978-3-031-20601-6_5

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One particularly impactful application of AI is its integration into facial recognition systems, especially in the context of software security (Smith, 2020). As we navigate the digital age, the significance of facial recognition, powered by AI, becomes increasingly apparent, positioning itself as a cornerstone in access control and authentication processes (Aboukadri et al, 2024;Dyson, 2022;Wilkinson, 2020).…”

Section: Literature Review System Architecturementioning

confidence: 99%

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Santoso,

Safitri,

Samidi

2024

SinkrOn

View full text Add to dashboard Cite

Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures

show abstract

Section: Literature Review System Architecturementioning

confidence: 99%

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Santoso,

Safitri,

Samidi

2024

SinkrOn

View full text Add to dashboard Cite

show abstract

“…Authentication, on the other hand, verifies the identity of individuals or systems seeking access, typically through credentials like usernames and passwords, biometrics, or multi-factor authentication methods. In the context of big data analytics, robust access controls and authentication mechanisms are essential for mitigating the risks of unauthorized access, insider threats, and data manipulation [111], [112]. By implementing these measures, organizations can establish a secure framework that governs data access and usage, fostering a proactive defense against potential security breaches and ensuring the confidentiality of sensitive information throughout the analytics lifecycle.…”

Section: Access Controls and Authenticationmentioning

confidence: 99%

Tackling security and privacy challenges in the realm of big data analytics

Janet Ngesa

2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

As organizations increasingly harness the power of big data analytics to derive insights and drive decision-making, the paramount concerns of security and privacy have come to the forefront. This paper presents a comprehensive framework for addressing the multifaceted challenges of big data security and privacy. Drawing on a synthesis of cutting-edge technologies, encryption methods, and access control mechanisms, our approach aims to fortify the entire big data lifecycle. The paper delves into innovative strategies for secure data storage, transmission, and processing, ensuring that sensitive information is shielded from unauthorized access or malicious attacks. Additionally, the framework incorporates robust privacy-preserving techniques, including anonymization and differential privacy, to uphold individual confidentiality. Through a meticulous analysis of current trends, emerging threats, and regulatory landscapes, this paper not only provides theoretical insights but also practical guidelines for organizations seeking to navigate the intricate landscape of big data while safeguarding the integrity, security, and privacy of the vast datasets at their disposal.

show abstract

“…Implementing strong protection measures, along with careful management of role and task information, serves as an important form of security-related metadata. Threat detection plays a crucial role in security monitoring, as it is the fundamental process through which we determine whether an activity is considered a potential threat [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing threat detection in Identity and Access Management (IAM) systems

Nikhil Ghadge

2024

Int. J. Sci. Res. Arch.

View full text Add to dashboard Cite

Identity and Access Management (IAM) systems play a pivotal role in safeguarding organizational resources by controlling access to sensitive information. However, these systems face evolving threats that can compromise security and privacy. This paper proposes a comprehensive approach to enhance threat detection within IAM systems. By integrating advanced techniques such as anomaly detection, machine learning, and behavior analysis, organizations can better identify and respond to suspicious activities. This paper discusses the challenges associated with threat detection in IAM systems and presents practical solutions to mitigate these risks. Furthermore, the paper highlights the importance of continuous monitoring and adaptation effectively combat emerging threats.

show abstract

Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Identity and Access Management Field: Challenges and State of the Art

Cited by 5 publications

References 26 publications

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Integration of Artificial Intelligence in Facial Recognition Systems for Software Security

Tackling security and privacy challenges in the realm of big data analytics

Enhancing threat detection in Identity and Access Management (IAM) systems

Contact Info

Product

Resources

About