A Supervised ML Biometric Continuous Authentication System for Industry 4.0

Abstract

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models.To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator's devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.

Behavioral Analysis Zero-Trust Architecture Relying on Adaptive Multifactor and Threat Determination

“…As to the field of behavioral authentication, Juan Manuel Espín López et al [29] first developed a supervised machine learning (ML) biometric continuous authentication system to verify staff and avoid security breaches using sensors, applications statistics, or speaker data in Industry 4.0. Md L. Ali et al [30] then designed a hybrid model with a partially observable hidden Markov model and support vector machine (POHMM/SVM) for keystroke biometric authentication, which is able to layout excellent performance and precisely handle the missing or irregular data.…”

MelSpectroNet: Enhancing Voice Authentication Security with AI-based Siamese Model and Noise Reduction for Seamless User Experience

“…The research by López et al [8] concentrates on developing and evaluating a Continuous Authentication (CA) system intended for Industry 4.0 environments, employing supervised machine learning methods. The main aim of this study is to tackle the difficulties related to the continuous and secure authentication of workers in industrial environments characterized by frequent interactions between workers and devices.…”

“…The uniqueness of voice traits between individuals, though reasonably high, is not perfect, so errors due to inter-speaker similarities may persist [12]. Most voice authentication systems also require users to speak fixed passphrases [8], which has usability drawbacks. Storage and potential leakage of voiceprints raise privacy concerns as well, motivating leakage-resilient designs like VOLERE [15].…”

“…Resource constraints pose challenges for on-device voice authentication on low-power IoT devices [1]. Continual authentication in dynamic industrial settings remains challenging [8]. Taken together, these issues emphasize that despite significant progress, there remains much room for improvement in making voice authentication systems more robust, usable, scalable, and privacy-preserving.…”