Buchumschlag

Fortified-Edge 2.0: Advanced Machine-Learning-Driven Framework for Secure PUF-Based Authentication in Collaborative Edge Computing

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Veröffentlicht in:Future Internet vol. 17, no. 7 (2025), p. 272-300
1. Verfasser: Aarella, Seema G
Weitere Verfasser: Yanambaka, Venkata P, Mohanty, Saraju P, Kougianos Elias
Veröffentlicht:
MDPI AG
Schlagworte:
Cryptography
Software
Collaboration
Protocol
Bandwidths
Real time
Edge computing
Cybersecurity
Data processing
Binary data
Privacy
Machine learning
High performance computing
Internet of Things
Smart cities
Computer centers
Data integrity
Infrastructure
Artificial intelligence
Authentication protocols
Confidentiality
Decision making
Energy efficiency
Authentication
Digital signatures
Security systems
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Abstract: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.
ISSN:1999-5903
DOI:10.3390/fi17070272
Quelle:ABI/INFORM Global