## 6 Discussion  
In this section, we discuss the results obtained. Firstly, the first scenario revealed that the ANN classifier outperforms Decision Tree, SVM, Random Forest and Naive Bayes classifiers in terms of accuracy percentage. Additionally, ANN, DT and SVM presented the best performance in terms of TP, precision and sensitivity metrics. However, the NB classifier showed the worst performance in terms of FP, precision and sensitivity metrics. Secondly, regarding the second scenario based on the provided BC access control data structure, the use of the proposed architecture led to an increase in the ML performance metrics in terms of MCC to detect DDoS, injection, brute force and XSS attacks thanks to extra features provided by the smart contract. The best results were given by the NB classifier to classify an XSS attack while the worst results were provided by RF and SVM classifiers to classify DDoS, injection and brute force attacks, respectively. Table 10 presents an ML metrics comparison for the studied classifiers versus a similar study in the literature [15] in terms of time for training (s), time for prediction (s) and accuracy. On the one hand, ANN is compared to the K-nearest neighbors (KNN) classifier, NB is compared to our work and in [15]. On the other hand, two classifiers based on ensemble model techniques such as AdaBoost and XGBoost [15] are compared as well. The XGBoost classifier is based on a genetic algorithm that is used essentially to find the differentiable loss function concern. As shown from Table 10, ANN outperforms the KNN [15] in terms of accuracy. However, it requires more time for training and prediction. The NB classifier in [15] presents an accuracy of 68.5% compared to 78.1% in our study. In addition, XGBoost [15] outperforms the AdaBoost classifier in terms of accuracy due to its capability to include an arbitrary loss function optimization. Nonetheless, XGBoost requires more time for prediction compared to the AdaBoost classifier. Some limitations of our work can be found. First of all, the number of experiments must be increased to enable future complete statistical studies; this paper contains only preliminary works that can only illustrate, at the writing of the paper, the potential benefits of the proposed architecture to mitigate various cyber-attacks in smart manufacturing applications. Several prospects can be identified. First, a more complete experimental protocol must be led to statistically prove the performance of our architecture, which was only illustrated in this paper on a limited set of experiments. Another prospect concerns the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **ANN** & **KNN [15]** & **NB** & **NB [15]** & **AdaBoost** & **XGBoost [15]** \\ \hline Time for & & & & & & \\ training (s) & 3232.14 & 1.119 & 0.55 & 1.115 & 3.81 & 1.412 \\ Time for & & & & & & \\ prediction (s) & 2.72 & 1.482 & 3.06 & 1.112 & 0.55 & 1.118 \\ Accuracy & 99.97 & 91.73 & 78.1 & 68.5 & 81.943 & 95.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ML metrics comparison for various classifiers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\%** & **Normal** & **DDoS** & **DoS** & **Injection** & **MitM** &
\begin{tabular}{c} **Brute** \\ **Force** \\ \end{tabular} & **XSS** & **Scanning** \\ \hline ANN & 100 & 100 & 99.3 & 100 & 100 & 100 & 100 & 99.7 \\ DT & 99.9 & 100 & 99.9 & 99.6 & 100 & 100 & 100 & 99.8 \\ SVM & 100 & 100 & 100 & 100 & 99.9 & 100 & 100 \\ RF & 99.6 & 99.8 & 99.3 & 99.7 & 57.7 & 100 & 99.7 & 99 \\ NB & 63.2 & 97.6 & 56.6 & 85.4 & 11.8 & 98.5 & 90.4 & 25.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MCC value per class for the studied classifiers related to the second scenario. implementation of an optimization technique such as ant colony optimization, genetic algorithm, heuristic and particle swarm optimization to improve and refine the ML layout analysis. Optimization technique can be a potential solution to enhance the performance metrics provided by the ML tools.