## 3 Related Prior Research
Features have been extracted using a modified VGG16 CNN from visual representations of PUF responses, and classification has been performed using lightweight models such as SVM and RF for both device- and group-level authentication. CRP database storage has been eliminated, and intra-group authentication has been enabled on resource-constrained devices. Achieving over 98% accuracy under varying environmental noise conditions, the system has been demonstrated as a robust and scalable ML-driven alternative for IoT security. A study involving ML to aid PUF-based authentication is proposed in [18]. This research proposes a lightweight authentication framework for the Internet of Medical Things (IoMT), integrating ML and PUFs to ensure data integrity and privacy. The proposed method eliminates the need for storing CRPs, reducing communication (68 bytes) and computation costs (2.33 ms). A machine learning-controlled PUF generates unpredictable responses, achieving 99.76% accuracy. The framework effectively mitigates impersonation, replay, and man-in-the-middle attacks while preserving anonymity and forward secrecy. Its efficiency and robustness make it suitable for resource-constrained medical devices, outperforming existing approaches in security and performance metrics. Furthermore, ML and AI have been employed along with PUF to enhance the security and reliability of PUF-based security systems. Some of the relevant research that addressed various security challenges is listed in Table 1. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Paper** & **Year** & **Algorithm** & **Application** & **Security Challenges Addressed** \\ \hline [PERSON] et al. [17] & 2023 & Deep CNN (VGG16), SVM/RF classifiers & Group-based authentication in IoT & Noise resilience, elimination of helper data, scalability \\ [PERSON] et al. [19] & 2023 & Delay-based ML-resistant PUF architecture & IoT device authentication & Resistance to ML modeling attacks, lightweight implementation \\ [PERSON] et al. [20] & 2024 & 4-layer DNN modeling PUFs with combinational logic & PUF vulnerability analysis & Exposure of security flaws in OR/AND logic-based PUFs \\ [PERSON] et al. [21] & 2024 & Supervised ML (e.g., RF, SVM) & Network intrusion detection in big data environments & Improved detection accuracy, reduced false positives \\ [PERSON] et al. [22] & 2024 & Supervised and unsupervised ML techniques & Network intrusion detection systems & Optimizing network security \\ [PERSON] et al. [23] & 2023 & Transformer-based sequence modeling & Real-time network intrusion detection & Timely detection and handling of sequential data threats \\ [PERSON] et al. [24] & 2023 & Federated learning with multiple ML models & Distributed intrusion detection across IoT systems & Data privacy, scalability, adaptability \\ [PERSON] et al. [25] & 2023 & Adversarial ML for botnet traffic simulation & IDS robustness evaluation & Exposure of ML-based IDS vulnerabilities, attack mitigation \\ [PERSON] et al. [26] & 2025 & Statistical feature selection + lightweight ML classifiers & Intrusion detection in constrained IoT systems & Efficient training, improved accuracy, lower computation \\ [PERSON] et al. [27] & 2024 & RF profiling integrated with PUF authentication & Remote keyless entry system security & Detection of unauthorized access, mutual authentication \\ Fortified-Edge 2.0 (Current Work) & 2025 & ML-based feature extraction and authentication & PUF-based distributed authentication protocol & Privacy protection, secure CRP handling, communication security \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table of PUF and ML-based cybersecurity research.