## 4 Results and Discussion
### Performance Analysis of the Proposed HT-RLSTM  
The proposed HT-RLSTM is evaluated in terms of various performance metrics, such as specificity, sensitivity, precision, and F-measure, and the final outcomes are compared with various existing works like KNN, ANFIS, SVM, and ANN in order to state the effectiveness of the model. The various performance metrics for attack detection are calculated as follows:  
\[\text{Accuracy}=\frac{\mathbf{TP}+\mathbf{TN}}{\mathbf{TP}+\mathbf{ FN}+\mathbf{FP}+\mathbf{TN}}\] \[\text{Precision}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FP}}\] \[\text{Sensitivity}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FN}}\] \[\text{Specificity}=\frac{\mathbf{TN}}{\mathbf{TN}+\mathbf{FP}}\]  
Table 2 comprises the value of the performance metrics, such as specificity, sensitivity, precision, and F-Measure of the proposed HT-RLSTM and the other existing works, like KNN, ANFIS, SVM, and ANN. The significance of the model is determined by the higher rate of performance metrics. As per the statement, the proposed method achieves 84.72% specificity, 95.45% sensitivity, 96.42% precision, and 97.23% of F-measure. However, the existing work obtains a specificity, sensitivity, precision, and F-Measure rate at an average of 90.34%, 89.04%, 86.37%, and 94.20%, respectively. This is lower as compared to the proposed work. Thus, the proposed HT-RLSTM mitigates various complexities and enhances the reliability of the cyber-attack detection process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Techniques**} & \multicolumn{6}{c}{**Attack Detection Rates (\%)**} \\ \cline{2-9}  & **DoS** & **Exploits** & **Generic** & **Reconnaissance** & **Shellcode** & **Zero Day** & **Worms** \\ \hline
**Proposed** & 97.65 & 98.46 & 96.12 & 95.36 & 94.75 & 98.56 & 96.82 \\
**HT-RLSTM** & 96.15 & 95.71 & 95.26 & 93.84 & 92.68 & 96.12 & 93.51 \\ \hline
**ANFIS** & 93.48 & 94.28 & 92.58 & 91.73 & 90.35 & 94.34 & 91.26 \\ \hline
**SVM** & 91.63 & 92.18 & 89.82 & 89.72 & 87.37 & 92.59 & 89.03 \\ \hline
**ANN** & 89.04 & 90.63 & 85.17 & 87.91 & 85.38 & 89.72 & 85.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance analysis of the proposed HT-RLSTM based on attack detection rate. A clear view of Table 2 is given in Figure 4. Figure 4 shows the comparative analysis of the proposed work. This comparative analysis clearly states that the proposed HT-RLSTM tends to attain higher specificity, sensitivity, precision, and F-measure values that range between 94.72-97.23%, whereas the existing techniques such as KNN, ANFIS, SVM, and ANN attain metrics values that, overall, range between 82.68-95.48%, which is comparatively lower than the HT-RLSTM. Hence, the proposed HT-RLSTM technique outperforms the other state-of-art methods and delivers more prominent results under various complex circumstances. Table 3 comprises the FPR, FNR, and MCC rates achieved by the proposed HT-RLSTM compared to existing works like KNN, ANFIS, SVM, and ANN. The significance of the model is determined by the low value of FPR and FNR rates and the high value of MCC. In accordance with the above-mentioned statement, the FPR and FNR rates of the proposed work are low, such as 1.82% and 3.03%, respectively. The MCC rate achieved by the proposed work is 95.16%, which is higher than the existing approaches. The existing approaches attain the FPR and FNR rates that, overall, range between 4.24-9.7% and 5.45-12.12%, respectively. Hence, the proposed method outperforms the other state-of-art methods and delivers better outcomes in the attack detection process.