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All Journal Formosa Journal of Multidisciplinary Research (FJMR)
Iffat Sania Hossain
California State University, Camarillo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Social Sciences

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Machine Learning-Driven Adaptive Authentication: Strengthening Cybersecurity against High-Volume Data Breaches Nisher Ahmed; Md Emran Hossain; Zakir Hossain; Md Farhad Kabir; Iffat Sania Hossain
Formosa Journal of Multidisciplinary Research Vol. 4 No. 2 (2025): February 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjmr.v4i2.49

Abstract

As cyberattacks become more frequent and sophisticated, traditional static authentication methods have failed to protect them against it. And so given that some high-volume data breach incidents have highlighted vulnerabilities inherent in traditional username/password authentication, we must abandon these notions and embrace adaptive machine-learning (ML)-driven authentication systems that dynamically alter system security based upon real-time risk assessment. To strengthen cybersecurity resilience, this study introduces an ML-driven adaptive authentication approach, in which behavioral biometric, contextual information analysis, and anomaly detection algorithms are leveraged. We use a deep risk assessment methodology that dynamically re-authenticates logins based on device characteristics, geo location histories, behavioural analytics and historical user behaviour.
Co-Authors Md Emran Hossain Md Farhad Kabir Nisher Ahmed Zakir Hossain