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.
Distributed denial-of-service (DDoS) attacks are a prevalent threat to resource-constrained IoT deployments. We present an edge-based detection and mitigation system integrated with the oneM2M architecture. By using a Raspberry Pi 4 client and five Raspberry Pi 3 attack nodes in…
Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase…
Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion…
Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices withi…
Handover management is a fundamental process in modern mobile networks, ensuring service continuity under user mobility. However, the relationship between network conditions and handover behavior remains insufficiently understood under real-world measurement conditions. This stud…
Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security th…