## 5 The Proposed Fortified-Edge 2.0 Framework  
To enhance the strength of the authentication system, this research proposes a novel authentication protocol that prioritizes data security. In the current scenario, where critical PUF CRP data are transmitted over a network, ensuring data integrity is essential to prevent compromise by external attacks. Cryptographic methods encrypt plaintext into ciphertext for secure communication. However, this research takes security further by uniquely processing the plaintext before transmission. Reliability is a crucial aspect of any security system utilizing PUFs. One of the key reliability challenges is the presence of bit errors in PUF responses, which can arise due to environmental variations. Therefore, integrating an effective bit error correction mechanism is essential for a robust PUF-based security system. In addition to enhancing security at the edge, the proposed framework aligns with broader sustainability objectives by emphasizing computational efficiency and energy awareness in IoT systems [54]. By leveraging Physically Unclonable Functions (PUFs) and lightweight cryptographic primitives such as ECDSA, the authentication process significantly reduces the need for intensive computation and large key storage, which are common drawbacks of traditional security protocols. The use of k-mer-based feature extraction further contributes to a low-overhead implementation by enabling efficient data representation and minimizing processing time. These design choices collectively lead to lower power consumption and reduced thermal load, which are critical for prolonging device lifespans and supporting scalable, energy-efficient deployments in smart city and smart village infrastructures. Therefore, the framework not only addresses the need for robust security in heterogeneous edge environments but also supports the long-term goal of sustainable IoT system design. An ML-based PUF bit error detection and correction method is proposed in this research [55]. This approach leverages an efficient K-mer sequencing method, which processes binary n-bit PUF responses as binary sequences and extracts relevant features. The ML model is trained on these features, enabling it to detect errors in new data. This method demonstrates high efficiency, achieving up to 99% accuracy. A key advantage of using ML is that it does not introduce additional area or computational overhead, unlike existing methods such as fuzzy extractors or error correction codes. The ML-based error correction process is illustrated in Figure 4. The error detection and correction process is shown in Algorithm 1. The n-bit binary PUF responses are transformed into binary sequences, referred to as words. A K-mer of 6 is applied to extract features, which are then vectorized using CountVectorizer(), into unique integer vectors. These vectors serve as references for bit error detection. K-mers algorithms are largely used for the analysis of genomic datasets, where K-mers represent a contiguous nucleotide or amino acid sequence of fixed-length K. K-mers are used in bioinformatics for sequence alignment, sequence clustering, error correction of sequencing reads, pattern recognition, and so on [56]. The error correction model uniquely transforms binary responses into integer vectors. By leveraging this transformed data without directly revealing the original n-bit response--a novel authentication protocol is implemented using feature vectors.