Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

Pritee, Zinniya Taffannum; Anik, Mehedi Hasan; Alam, Saida Binta; Jim, Jamin Rahman; Kabir, Md Mohsin; Mridha, M.F.

doi:10.1016/j.cose.2024.103747

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2024

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Cited by 5 publications

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Integrating machine learning for sustaining cybersecurity in digital banks

Asmar,

Tuqan

2024

Heliyon

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Integrating machine learning for sustaining cybersecurity in digital banks

Asmar,

Tuqan

2024

Heliyon

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CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication

Wazzeh,

Arafeh,

Sami

et al. 2024

Journal of Network and Computer Applications

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Harnessing Machine Learning Intelligence Against Cyber Threats

Singh,

Kaunert,

Gautam

2024

Advances in Business Strategy and Competitive Advantage

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The spread of cyberthreats in the digital age presents serious concerns to national security, the stability of the economy, and personal privacy. Traditional security methods are unable to keep up with the increasing sophistication and size of cyberattacks. With facilitating quick identification and mitigation of cyberthreats, machine learning (ML) has the revolutionary potential to improve cybersecurity measures. But applying ML in this field also brings up important moral and legal issues, particularly in light of international cybercrimes. This chapter comprehensively explores the machine learning's dual nature in cybersecurity, emphasizing both its advantages and disadvantages. It also talk about the state of cyber threats today, how machine learning is being incorporated into cybersecurity, and the moral and legal ramifications of using ML to international cyber investigations.

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Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

Cited by 5 publications

References 124 publications

Integrating machine learning for sustaining cybersecurity in digital banks

Integrating machine learning for sustaining cybersecurity in digital banks

CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication

Harnessing Machine Learning Intelligence Against Cyber Threats

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