## 5 The Proposed Fortified-Edge 2.0 Framework
The feature-based authentication system, which integrates machine learning and secure authentication protocols, is illustrated in Figure 5.  
```
0: Dataset \(\mathcal{D}\) of \(N\) binary sequences, K-mer size \(k\)
0: Trained classifier and evaluation metrics
1: Step 1: Load and Preprocess Data
2: Read each sequence from \(\mathcal{D}\) into list \(S=\{s_{1},s_{2},\ldots,s_{N}\}\)
3: Initialize DataFrame \(DF\) with column sequence\(\gets S\)
4: Step 2: Assign Class Labels
5: For each index \(i\in[0,N-1]\), compute class label \(c_{i}=\lfloor i/100\rfloor+1\)
6: Add column \(DF[\texttt{class}]\gets c_{i}\)
7: Step 3: Extract K-Mers and Vectorize
8: For each sequence \(s_{i}\) in \(DF[\texttt{sequence}]\):
9: Generate overlapping K-mers \(K_{i}=\{s_{i}[j:j+k]\mid 0\leq j\leq|s_{i}|-k\}\)
10: Concatenate \(K_{i}\) into space-separated string \(w_{i}\)
11: Set \(DF[\texttt{words}_{i}]\gets w_{i}\)
12: Drop original column \(DF[\texttt{sequence}]\)
13: Step 4: Feature Transformation
14: Apply CountVectorizer with n-gram size 4 on \(DF[\texttt{words}]\) to obtain matrix \(X\)
15: Step 5: Split Dataset
16: Let \(y\gets DF[\texttt{class}]\)
17: Split \(X,y\) into training and testing sets:
18: \(X_{\text{train}},X_{\text{test}},y_{\text{train}},y_{\text{test}}\)
19: Step 6: Train and Evaluate Classifier
20: Train Multinomial Naive Bayes model on \((X_{\text{train}},y_{\text{train}})\)
21: Evaluate performance on \(X_{\text{test}}\) using accuracy and other metrics The proposed framework illustrates the preprocessing of PUF CRP data and feature extraction using an ML model. Additionally, the model is implemented in a distributed framework, making it suitable for PUF-based security systems deployed in collaborative computing ecosystems at the edge [57]. First, the CRP dataset is used to train the ML model, which is then deployed across multiple edge servers. The distributed framework is implemented using Federated Learning, where each local model is trained on its respective PUF data. Features are extracted and vectorized, and the local model parameters are sent to a global server, which aggregates them using FedAvg. The updated model is then distributed back to the local models at the edge. This distributed error detection and correction system is utilized to authenticate edge devices. In the current scenario, the application considered is Edge Data Centers participating in load balancing at the edge. These EDCs must be authenticated in real time before task offloading. The proposed authentication protocol operates in multiple stages. In the first stage, Client_1 (EDC-1) initiates authentication with Client_2 (EDC-2) to enable task sharing. The selected challenge is vectorized, encrypted using private keys, and transmitted over the network to Client_2. Upon receiving the encrypted challenge, Client_2 decrypts it and reconstructs the original n-bit challenge from its vectors using the local Federated Learning (FL) model. The reconstructed challenge is then applied to the PUF module, which generates the corresponding n-bit response. The local ML model at Client_2 vectorizes the response, encrypts the vectors, and transmits them back to Client_1. Figure 4: Error detection and correction model. Figure 5: The proposed process flow of feature-based authentication. Client_1 decrypts the received vectors using its public key and reconstructs the original n-bit response. The extracted response is then verified against the expected value. If the verification is successful, Client_2 is authenticated, and both clients proceed with task sharing. The proposed method eliminates the vulnerability of transmitting crucial PUF data in its original form, improves the data security, and maintains the integrity of the system. A generalized representation of the combined process flow of the PUF module along with the ML model for secure authentication is shown in Figure 6.

Client_1 initiates an authentication request. Client_2's ML model extracts the feature vector of a challenge in response to an authentication request initiated by Client_1.