## 4 Performance Evaluation
### Experimental Results and Discussion  
In this section, we focus on analyzing the performance of the proposed MSE classifier by benchmarking it against a variety of individual algorithms, as well as comparing it with state-of-the-art methods. The model was evaluated using Python within the Jupyter Notebook environment. In our experiments, we used such libraries as Scikit-learn, Pandas, and NumPy. Scikit-learn is an efficient machine learning and data modeling tool utilized for the purpose of data analysis, whereas NumPy and Pandas are machine learning and deep learning libraries dealing with data structure management, data manipulation and analysis, and computational processing [76]. Our experiments were conducted on a system with the following hardware and software specifications:  
* Windows 11 (64-bit) as the operating system. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Parameters** \\ \hline \multirow{2}{*}{DT} & Criterion = ‘entropy’, min\_samples\_leaf = 5, \\  & min\_samples\_split = 2. \\ \multirow{2}{*}{Hist-gradient boosting} & Loss = ‘categorical\_crossentropy’, min\_samples\_leaf = 10, \\  & learning\_rate = 0.1. \\ \multirow{2}{*}{RF} & N\_estimators = 100, criterion = ‘entropy’, max\_depth = 60. \\ \multirow{2}{*}{MLP} & Hidden\_layer\_sizes with 100 neurons, activation = ‘relu’, \\  & solver = ‘adam’. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental model parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Category of Attack** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{8}{*}{**DT**} & Analysis & 66.66 & 10.09 & 17.53 \\  & Backdoor & 50.81 & 6.65 & 11.76 \\  & DoS & 43.90 & 17.05 & 24.57 \\  & Exploits & 61.59 & 89.10 & 72.84 \\  & Fuzzers & 71.52 & 53.51 & 61.22 \\  & Generic & 99.17 & 97.76 & 98.46 \\  & Normal & 90.32 & 94.05 & 92.15 \\  & Reconnaissance & 89.25 & 74.51 & 81.22 \\  & Shellcode & 54.16 & 51.65 & 52.88 \\  & Worms & 33.33 & 11.42 & 17.02 \\  & Analysis & 74.68 & 11.02 & 19.21 \\  & Backdoor & 52.27 & 4.93 & 9.01 \\  & DoS & 45.62 & 11.46 & 18.32 \\  & Exploits & 61.12 & 91.04 & 73.14 \\  & Fuzzers & 75.71 & 55.31 & 63.92 \\  & Generic & 99.40 & 98.03 & 98.71 \\  & Normal & 90.64 & 95.08 & 92.81 \\  & Reconnaissance & 90.50 & 73.90 & 81.36 \\  & Shellcode & 53.82 & 58.27 & 55.96 \\  & Worms & 16.66 & 22.85 & 19.27 \\  & Analysis & 69.56 & 11.96 & 20.41 \\  & Backdoor & 64 & 6.86 & 12.40 \\  & DoS & 34.06 & 23.23 & 27.62 \\  & Exploits & 63.41 & 82.63 & 71.76 \\  & Fuzzers & 70.92 & 59.31 & 64.60 \\  & Generic & 99.63 & 97.92 & 98.77 \\  & Normal & 91.64 & 94.98 & 93.28 \\  & Reconnaissance & 89.96 & 75.91 & 82.34 \\  & Shellcode & 63.95 & 59.93 & 61.88 \\  & Worms & 33.33 & 8.50 & 13.63 \\  & Analysis & 65.16 & 10.84 & 18.58 \\  & Backdoor & 57.62 & 7.29 & 12.95 \\  & DoS & 44.25 & 25.31 & 32.20 \\  & Exploits & 63.79 & 88.26 & 74.06 \\  & Fuzzers & 73.54 & 59.22 & 65.61 \\  & Generic & 99.44 & 98.06 & 98.74 \\  & Normal & 91.75 & 94.15 & 92.93 \\  & Reconnaissance & 91.35 & 75.12 & 82.44 \\  & Shellcode & 64.94 & 58.27 & 61.43 \\  & Worms & 27.27 & 17.14 & 21.05 \\  & Analysis & 81.14 & 26.54 & 40 \\  & Backdoor & 72.80 & 17.81 & 28.62 \\  & DoS & 59.64 & 49.25 & 53.95 \\  & Exploits & 73.74 & 90.06 & 81.08 \\  & Fuzzers & 89.32 & 84.36 & 86.77 \\
**Proposed model (MSE)** & Generic & 99.82 & 99.06 & 99.44 \\  & Normal & 98.19 & 98.37 & 98.28 \\  & Reconnaissance & 95.93 & 82.70 & 88.82 \\  & Shellcode & 87.22 & 92.71 & 89.88 \\  & Worms & 76.92 & 85.71 & 81.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation of multiclass classification results for \(D1\). Individual classifiers exhibited varying success rates in identifying different attack classes. The DT, hist-gradient boosting, RF, and MLP algorithms showed suboptimal performance in detecting Analysis, Backdoor, DoS, and Worm attacks. In particular, the MLP demonstrated better capability in identifying DoS and Worm attacks compared to other individual algorithms.