## 3 Related Prior Research  
PUFs have emerged as lightweight hardware security primitives, offering a robust solution for ensuring system integrity and proving authenticity through their inherent uniqueness and unclonability. While PUFs demonstrate resilience against various external threats, the rapid advancement of technology has introduced sophisticated attack vectors aimed at compromising such systems. Consequently, recent research has focused on enhancing PUF-based security by integrating cutting-edge technologies, such as ML and Artificial Intelligence (AI), to reinforce their robustness and adaptability in modern threat landscapes. Research [12] indicates that PUFs, due to their inherent properties such as irreproducibility, uniqueness, obfuscation, and unpredictability, are highly effective in mitigating a wide range of attacks. These include man-in-the-middle, side-channel, replay, spoofing, reverse engineering, intellectual property hijacking, hardware Trojan insertion, counterfeit hardware, Sybil attacks, denial-of-service (DoS), node capture, and routing-based attacks. Moreover, PUFs eliminate the need to store cryptographic keys in memory, making them particularly suitable for resource-constrained environments due to their low-cost and high-security characteristics. A study on the use of PUFs for secure authentication in edge data centers demonstrates that PUFs provide an efficient and lightweight solution for implementing security at the edge [13]. By combining effective CRP management with the principles of SbD, PUFs enable security to be integrated as a foundational component of the system. This approach facilitates the creation of secure operating environments with continuous monitoring and enforcement of security throughout the entire life cycle of the device. ML techniques have been employed to model PUF behavior, which helps in understanding and improving PUF-based security. In the research [14], a deep neural network attack on arbiter-PUF has been studied to identify the vulnerabilities and strengthen the defense. The research shows that deep neural network (DNN) effectively models OAX-PUFs, revealing security vulnerabilities in combinational logic-based PUF designs and calling for new defenses against ML-based attacks. A study proposes a novel Virtual PUF (VPUF) authentication scheme for IoT networks using a split learning-based encoder-decoder architecture. By offloading computation to the server and transmitting latent representations instead of raw PUF responses, the VPUF reduces power consumption, maintenance, and processing overhead on resource-constrained devices [15]. It addresses the limitations of hardware PUFs, such as aging effects and production costs. Experimental results show the scheme achieves 100% authentication accuracy, even under noisy conditions, effectively emulating hardware PUF behavior while ensuring secure and efficient authentication in IoT environments. Novel research involving 5G secure handover authentication protocol using Spiking Neural Networks and Fuzzy Logic (SNN-FL) to enhance cybersecurity in mobile networks is proposed in [16]. The protocol mitigates attacks such as man-in-the-middle(MITM), replay, desynchronization, and DoS while ensuring forward and backward key secrecy. It significantly reduces communication overhead, handover latency, and packet loss compared to existing 5G-AKA and 3 GPP R16 protocols. Experimental results show the proposed method achieves high accuracy (98%), precision (0.97), recall (0.97), and F1-score (0.98), making it an efficient and robust solution for secure communication in large-scale, resource-constrained 5G environments. A machine learning-based authentication framework, PUF-Phenotype, has been proposed in [17], utilizing noisy DRAM-PUF responses for secure device identification without reliance on helper data or traditional error correction.