Fortified-Edge 2.0: Advanced Machine-Learning-Driven Framework for Secure PUF-Based Authentication in Collaborative Edge Computing
محفوظ في:
| الحاوية / القاعدة: | Future Internet vol. 17, no. 7 (2025), p. 272-300 |
|---|---|
| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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MARC
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| 024 | 7 | |a 10.3390/fi17070272 |2 doi | |
| 035 | |a 3233189365 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231464 |2 nlm | ||
| 100 | 1 | |a Aarella, Seema G |u Department of Computer Science, Austin College, Sherman, TX 75090, USA | |
| 245 | 1 | |a Fortified-Edge 2.0: Advanced Machine-Learning-Driven Framework for Secure PUF-Based Authentication in Collaborative Edge Computing | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This research introduces Fortified-Edge 2.0, a novel authentication framework that addresses critical security and privacy challenges in Physically Unclonable Function (PUF)-based systems for collaborative edge computing (CEC). Unlike conventional methods that transmit full binary Challenge–Response Pairs (CRPs) and risk exposing sensitive data, Fortified-Edge 2.0 employs a machine-learning-driven feature-abstraction technique to extract and utilize only essential characteristics of CRPs, obfuscating the raw binary sequences. These feature vectors are then processed using lightweight cryptographic primitives, including ECDSA, to enable secure authentication without exposing the original CRP. This eliminates the need to transmit sensitive binary data, reducing the attack surface and bandwidth usage. The proposed method demonstrates strong resilience against modeling attacks, replay attacks, and side-channel threats while maintaining the inherent efficiency and low power requirements of PUFs. By integrating PUF unpredictability with ML adaptability, this research delivers a scalable, secure, and resource-efficient solution for next-generation authentication in edge environments. | |
| 653 | |a Cryptography | ||
| 653 | |a Software | ||
| 653 | |a Collaboration | ||
| 653 | |a Protocol | ||
| 653 | |a Bandwidths | ||
| 653 | |a Real time | ||
| 653 | |a Edge computing | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Data processing | ||
| 653 | |a Binary data | ||
| 653 | |a Privacy | ||
| 653 | |a Machine learning | ||
| 653 | |a High performance computing | ||
| 653 | |a Internet of Things | ||
| 653 | |a Smart cities | ||
| 653 | |a Computer centers | ||
| 653 | |a Data integrity | ||
| 653 | |a Infrastructure | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Authentication protocols | ||
| 653 | |a Confidentiality | ||
| 653 | |a Decision making | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Authentication | ||
| 653 | |a Digital signatures | ||
| 653 | |a Security systems | ||
| 700 | 1 | |a Yanambaka, Venkata P |u School of Sciences, Texas Woman’s University, Denton, TX 76204, USA; vyanambaka@twu.edu | |
| 700 | 1 | |a Mohanty, Saraju P |u Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA; saraju.mohanty@unt.edu | |
| 700 | 1 | |a Kougianos Elias |u Department of Electrical Engineering, University of North Texas, Denton, TX 76203, USA; elias.kougianos@unt.edu | |
| 773 | 0 | |t Future Internet |g vol. 17, no. 7 (2025), p. 272-300 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233189365/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233189365/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233189365/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |