Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | A Supervised ML Biometric Continuous Authentication System for Industry 4.0 |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
|
| Journal Title | IEEE Transactions on Industrial Informatics |
| Publisher | Institute of Electrical and Electronics Engineers |
| Geographical Reach | international |
| ISSN | 1551-3203 |
| Volume | 18 |
| Number | 12 |
| Page Range | 9132 - 9140 |
| Date | 2022 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1109/TII.2022.3171321 |
| Other Identification Number | merlin-id:23183 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|
| Keywords | Applications usage, continuous authentication(CA), Industry 4.0, machine learning (ML)/deep learning(DL), sensors, speaker recognition |
| Additional Information | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |