## 5 The Proposed Fortified-Edge 2.0 Framework
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research** & **ML Algorithm** & **Application** & **Cryptography Algorithm** & **Auth. Time (s)** & **Bandwidth (Kbps)** & **Accuracy (\%)** & **Energy Efficiency \& Sustainability** & **Scalability** \\ \hline
[17] & LR and RF classifiers, CNN for feature extraction & PUF behavior authentication & NA & NA & NA & 98.4 & No real-time inference, high energy efficiency & Group-based DRAM-PUF limits scalability \\ \hline
[21] & DT, RF and Extra Tree & Network intrusion detection & NA & NA & NA & 99.99 & Lacks energy profiling, moderately sustainable & High, tested on various benchmark datasets \\ \hline
[22] & RF, DT, KNN, MLP, ANN, CNN & Network intrusion detection & Multi-factor authentication & NA & NA & 99.97 & Moderate energy efficiency, designed to reduce long-term environmental impact & Supports distributed ML and scalable IDS \\ \hline
[23] & Decision Transformer & Network intrusion detection & NA & NA & NA & 99.33 & High energy efficiency, high sustainability & DTs scale well with data \\ \hline
[27] & CNN & Remote keyless entry & XOR-based secret key encryption & 2.05 & NA & 99.8 & Moderate energy efficiency, Higher hardware dependency reduces sustainability & Limited to vehicle specific setup \\ \hline
[58] & SVM & Authentication Monitoring & PUF-based Authentication & NA & NA & 99 & Design optimized for low resource usage & Designed for collaborative environment \\ \hline Fortified-Edge 2.0 (Current work) & K-mer & PUF CRP Feature Extraction & ECDSA & 0.028031 & 368 & 99.74 & Lightweight ECDSA and ML model for high energy efficiency and high sustainability & Feature-based encoding supports distributed ecosystem \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results with state-of-the-art literature. a secure, lightweight, and robust solution within the HAS paradigm, particularly well-suited for optimization in resource-constrained environments such as collaborative edge computing in smart village infrastructures. ML techniques further strengthen the security, integrity, and reliability of PUF-based systems. This research focuses on improving the reliability and security of device identification and data communication by proposing a novel feature-based authentication protocol. By transmitting sensitive CRP data in a vectorized form, the protocol ensures that critical information remains concealed from potential adversaries within the network. The k-mer-based feature extraction model efficiently derives distinctive features from binary challenge and response sequences and supports accurate reconstruction of the original sequences from the extracted vectors. This method enhances the confidentiality and integrity of transmitted data. Statistical analysis of the extracted feature vectors demonstrates strong correlation and high entropy levels, characteristics essential for reliable PUF operation in real-world applications. The use of PUF eliminates the need for storing and retrieving cryptographic keys, significantly reducing the computational complexity and associated power overhead. Additionally, the ECDSA algorithm is selected over RSA for shorter and more secure keys, contributing to reduced computational cost and energy usage. The k-mer-based feature extraction further reduces the dimensionality of the data, enabling faster computation. Performance evaluation shows that the proposed protocol is computationally efficient, consuming minimal CPU resources and achieving execution times of approximately 28 ms. Additionally, bandwidth consumption is measured at around 368 Kbps, highlighting the protocol's suitability for deployment in resource-constrained environments. Future work includes evaluating the protocol's resilience against various external attack models, validating its performance across different hardware platforms, and enhancing the design to offer robustness against quantum adversaries. Conceptualization, S.P.M. ; Investigation, S.G.A.