## 5 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Device/** & **Attack** & **Algorithm** & **Accuracy** & **TP** & **FP** & **FN** & **TN** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{9}{*}{TCP} & RF & 0.999943 & 11973 & 3 & 2 & 2012 & 0.999849 & 0.999833 & 0.999891 & 0.999954 \\  & DT & 0.999714 & 11942 & 2 & 2 & 2044 & 0.999833 & 0.999833 & 0.999833 & 0.999876 \\  & kNN & 0.999571 & 11919 & 2 & 4 & 2065 & 0.999832 & 0.999665 & 0.999748 & 0.999744 \\  & XGBOOST & 0.999786 & 11903 & 1 & 2 & 2084 & 0.999916 & 0.999832 & 0.999874 & 0.999962 \\ \cline{2-11}  & \multirow{4}{*}{TCP} & RF & 0.999815 & 4494 & 4 & 5 & 1997 & 0.999889 & 0.999993 & 0.999972 \\ \cline{1-1}  & \multirow{4}{*}{UDP} & RF & 0.998923 & 4459 & 2 & 5 & 2034 & 0.999552 & 0.99888 & 0.999216 & 0.998947 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & kNN & 0.999538 & 4435 & 2 & 1 & 2062 & 0.999549 & 0.999775 & 0.999662 & 0.999642 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & XGBOOST & 0.998923 & 4400 & 3 & 4 & 2093 & 0.999319 & 0.9999092 & 0.999205 & 0.9999741 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & SVM & 0.996769 & 4420 & 6 & 12 & 4420 & 0.99849 & 0.997548 & 0.998019 & 0.9988666 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & RF & 0.999784 & 21087 & 3 & 2 & 2008 & 0.999858 & 0.999905 & 0.999881 & 0.999862 \\ \cline{1-1}  & \multirow{4}{*}{HTTP} & DT & 0.99961 & 21042 & 4 & 5 & 2049 & 0.99981 & 0.999762 & 0.999786 & 0.999649 \\ \cline{1-1}  & \multirow{4}{*}{GT} & k \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Device/** & **Attack** & **Algorithm** & **Accuracy** & **TP** & **FP** & **FN** & **TN** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{9}{*}{TCP} & RF & 0.999067 & 5484 & 5 & 2 & 2009 & 0.999889 & 0.999735 & 0.999862 & 0.999807 \\  & DT & 0.999333 & 5457 & 4 & 1 & 2038 & 0.999268 & 0.999817 & 0.999542 & 0.999866 \\  & kNN & 0.999067 & 5440 & 2 & 5 & 2053 & 0.999632 & 0.999082 & 0.999357 & 0.999787 \\  & XGBOOST & 0.9988 & 5409 & 4 & 5 & 2082 & 0.999261 & 0.999076 & 0.999169 & 0.999392 \\  & SVM & 0.9972 & 5386 & 9 & 12 & 2093 & 0.998332 & 0.99777 & 0.998054 & 0.998168 \\ \hline \multirow{9}{*}{UDP} & RF & 0.99988 & 10188 & 5 & 5 & 2002 & 0.99988 & 0.99988 & 0.99988 & 0.99988 \\  & DT & 0.999344 & 10146 & 3 & 5 & 2046 & 0.999704 & 0.999507 & 0.999606 & 0.999544 \\  & kNN & 0.999262 & 10140 & 5 & 4 & 2051 & 0.999507 & 0.999606 & 0.999556 & 0.999830 \\  & XGBOOST & 0.999344 & 10130 & 5 & 3 & 2062 & 0.999507 & 0.999704 & 0.999605 & 0.999510 \\ Thermostat/ & SVM & 0.998033 & 10073 & 3 & 16 & 10073 & 0.99849 & 0.996984 & 0.997736 & 0.998590 \\ \cline{2-11}  & RF & 0.999633 & 19765 & 3 & 5 & 2017 & 0.999848 & 0.999747 & 0.999798 & 0.999937 \\  & DT & 0.999541 & 19730 & 5 & 5 & 2050 & 0.999747 & 0.999747 & 0.999747 & 0.999730 \\  & kNN & 0.999725 & 19716 & 2 & 4 & 2068 & 0.999899 & 0.999797 & 0.999848 & 0.999950 \\  & XGBOOST & 0.999679 & 19680 & 3 & 4 & 2103 & 0.999848 & 0.999797 & 0.999822 & 0.999980 \\  & SVM & 0.999082 & 19644 & 4 & 13 & 19644 & 0.998182 & 0.997275 & 0.997728 & 0.999110 \\ \hline \multirow{9}{*}{DNS} & RF & 0.999649 & 9381 & 3 & 1 & 2015 & 0.99968 & 0.999893 & 0.999787 & 0.999683 \\  & DT & 0.999737 & 9345 & 1 & 2 & 2052 & 0.999893 & 0.999786 & 0.99984 & 0.999960 \\ \cline{1-1}  & kNN & 0.999211 & 9335 & 4 & 5 & 2056 & 0.999572 & 0.999465 & 0.999518 & 0.999830 \\ \cline{1-1}  & XGBOOST & 0.999561 & 9305 & 3 & 2 & 2090 & 0.999678 & 0.999785 & 0.999731 & 0.999730 \\ \cline{1-1}  & SVM & 0.998421 & 9339 & 4 & 13 & 2044 & 0.998379 & 0.996763 & 0.99757 & 0.999860 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Classification results (thermostat—Dark Nexus).