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

doi:10.55927/fjmr.v4i2.49

Authors

Nisher Ahmed Westcliff University
Md Emran Hossain Westcliff University
Zakir Hossain California State University, Northridge
Md Farhad Kabir University of Southern California
Iffat Sania Hossain California State University, Camarillo

DOI:

https://doi.org/10.55927/fjmr.v4i2.49

Keywords:

Cyberattacks, Authentication, Machine Learning, Adaptive Systems, Behavioral Biometrics

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.

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