## 6 Results  
Utilizing a range of machine learning algorithms, we performed experiments by dividing 80% of the dataset for training the system while allocating the remaining 20% to evaluate its performance. The feature selection techniques included Ant Colony Optimization (ACO) algorithms, the Flower Pollination Algorithm (FPA), and Artificial Bee Colony (ABC). Subsequently, these selected features were tested and evaluated with various machine learning algorithms. Tables 8-10 (values rounded to three decimal places) display the outcomes of these tests. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Attack Types** \\ \hline Benign & \\ \hline \multirow{3}{*}{DDoS} & HOIC \\ \cline{2-3}  & LOIC-HTTP \\ \cline{2-3}  & LOIC-UDP \\ \hline Botnet & Bot \\ \hline \multirow{3}{*}{Brute Force} & FTP \\ \cline{2-3}  & SSH \\ \cline{2-3}  & Infiltration \\ \hline \multirow{3}{*}{Dos} & Hulk \\ \cline{2-3}  & SlowHTTPTest \\ \cline{2-3}  & GoldenEye \\ \cline{2-3}  & Slowloris \\ \hline \multirow{3}{*}{Web Attack} & Web \\ \cline{2-3}  & XSS \\ \cline{2-3}  & SQL Injection \\ \hline \end{tabular}
\end{table} 
Table 6: Attack types and secure traffic in the dataset CSE-CIC-IDS-2018. \begin{table}
\begin{tabular}{c c c} \hline
**Traffic Class** & **Record Count** & **Share (\%)** \\ \hline Benign & 13,484,708 & 83.070\% \\ \hline HOIC & 686,012 & 4.226\% \\ \hline LOIC-HTTP & 576,191 & 3.550\% \\ \hline Hulk & 461,912 & 2.846\% \\ \hline Bot & 286,191 & 1.76\% \\ \hline FTP—BruteForce & 193,360 & 1.191\% \\ \hline SSH—BruteForce & 187,589 & 1.156\% \\ \hline Infiltration & 161,934 & 0.998\% \\ \hline SlowHTTPTest & 139,890 & 0.862\% \\ \hline GoldenEye & 41,508 & 0.256\% \\ \hline Slowloris & 10,990 & 0.068\% \\ \hline LOIC-UDP & 1730 & 0.011\% \\ \hline Brute Force—Web & 611 & 0.004\% \\ \hline Brute Force—XXS & 230 & 0.001\% \\ \hline SQL Injection & 87 & 0.0005\% \\ \hline \end{tabular}
\end{table} 
Table 7: The CSE-CIC-IDS2018 dataset’s record count. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Time Spent on a** \\ **Model Build** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline AdaBoostM1 & 802.44 s & 0.862 & 0.961 & 0.883 & 0.920 \\ \hline Bagging & 13,969.96 s & 0.988 & 0.996 & 0.989 & 0.992 \\ \hline BayesNet & 638.07 s & 0.965 & 0.968 & 0.989 & 0.979 \\ \hline CDT & 1132.89 s & 0.987 & 0.997 & 0.988 & 0.993 \\ \hline DTNB & 18,733.06 s & 0.986 & 0.996 & 0.988 & 0.992 \\ \hline IB1 & 9 s & 0.978 & 0.986 & 0.988 & 0.987 \\ \hline J48 & 2858.5 s & 0.988 & 0.997 & 0.988 & 0.993 \\ \hline KStar & 3 s & 0.979 & 0.989 & 0.987 & 0.988 \\ \hline Local Knn & 324,075 s & 0.984 & 0.993 & 0.988 & 0.990 \\ \hline Logistic & 35,662 s & 0.960 & 0.981 & 0.972 & 0.976 \\ \hline LogitBoost & 2752 s & 0.982 & 0.993 & 0.985 & 0.989 \\ \hline Multilayer perceptron & 17,072 s & 0.945 & 0.968 & 0.965 & 0.967 \\ \hline Random Tree & 481.24 s & 0.983 & 0.991 & 0.989 & 0.990 \\ \hline SMO & 48,562 & 0.891 & 0.993 & 0.889 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of different algorithms using features selected by the Ant Colony Optimization (ACO) algorithms.