## 5 The Proposed Fortified-Edge 2.0 Framework
* Side-Channel Attacks: By transmitting only the abstracted feature vectors rather than raw binary data, the protocol minimizes the information exposed over the channel. This design inherently reduces side-channel leakage that could otherwise be exploited to infer the internal PUF behavior or secret keys. * Cloning Attacks: The use of PUFs ensures that device identities are physically bound and unclonable due to inherent hardware variations. Even if the attacker observes multiple authentication sessions, reproducing the exact behavior of the device is not possible without access to the original hardware. ### Comparative Perspective

The superiority of the current research is demonstrated through significant enhancements over the preliminary Fortified-Edge 2.0 framework [58]. The current work addresses critical security challenges by introducing a secure, low-latency, and low-bandwidth authentication protocol tailored for resource-constrained edge environments. A comparative \begin{table}
\begin{tabular}{l c c} \hline
**Metric** & **Server Side** & **Client Side** \\ \hline Total Execution Time (ms) & 28.031 & 28.031 \\ \hline Challenge Signing Time (ms) & 1.067 & - \\ \hline Challenge Receiving Time (ms) & - & 3.133 \\ \hline Response Receiving Time (ms) & 3.952 & - \\ \hline Response Signing Time (ms) & - & 1.002 \\ \hline Response Sending Time (ms) & - & 0.000 \\ \hline Signature Verification Time (ms) & 2.003 & 1.948 \\ \hline CPU Usage (\%) & 32.40 & 36.90 \\ \hline Original Challenge Size (bytes) & 105 & - \\ \hline Vectorized Challenge Size (bytes) & 662 & - \\ \hline Original Response Size (bytes) & - & 105 \\ \hline Vectorized Response Size (bytes) & 626 & - \\ \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of server and client. analysis of the Fortified-Edge research with state-of-the-art research from Table 1 is presented in Table 5.

The preliminary work presents a machine learning-based authentication monitoring system for the EDC, which continuously monitors the authentication process using parameters such as location, EDC ID, site ID, authentication time, and others. Threats are detected by identifying anomalies in these parameters and flagging them as potentially malicious requests. While the preliminary work ensures security by monitoring incoming authentication requests and processes, it does not address broader security challenges, such as data confidentiality and privacy during communication. It does not meet the requirements for a low-latency, low-bandwidth authentication protocol essential for resource-constrained edge environments. In contrast, the current work effectively addresses these challenges by introducing a secure, lightweight authentication protocol based on machine learning-driven PUF feature extraction. This approach ensures that critical PUF CRP data remains protected during transmission, even across untrusted communication networks. The results from state-of-the-art literature show the efficient use of ML for security solutions in various applications. However, using a feature-based authentication process as proposed by the Fortified-Edge 2.0 research is novel. ## 7 Conclusions and Future Research

Security solutions leveraging embedded hardware provide strong protection without adding complexity to the system architecture. HAS is an emerging area that enhances privacy and security by incorporating SbD principles, embedding security as an integral and continuous process throughout the life cycle of a device or system. PUFs represent