## 4 Experimental Results and Analysis
### Dataset Description
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Feature** & **Data Type** & **Feature** & **Data Type** & **Feature** & **Data Type** & **Feature** & **Data Type** \\ \hline Flow\_ID & Categorical & Flow\_LAT\_Mean & Float & Pkt\_Len\_Max & Float & Subflow\_Fwd\_Pkts & Integer \\ Src\_IP & Categorical & Flow\_LAT\_Std, & Float & Pkt\_Len\_Mean & Float & Subflow\_Fwd\_Byts & Integer \\ Src\_Port & Integer & Flow\_LAT\_Max & Float & Pkt\_Len\_Std & Float & Subflow\_Bwd\_Pkts & Integer \\ Dst\_IP & Categorical & Flow\_LAT\_Min & Float & Pkt\_Len\_Var & Float & Subflow\_Bwd\_Byts & Integer \\ Dst\_Port & Integer & Flow\_LAT\_Tot & Float & FIN\_Flag\_Cnt & Integer & Init\_Fwd\_Wm\_Byts & Integer \\ Protocol & Integer & Fwd\_LAT\_Mean & Float & SYN\_Flag\_Cnt & Integer & Init\_Bwd\_Wm\_Byts & Integer \\ Timestamp & Categorical & Bud\_LAT\_Mean & Float & RST\_Flag\_Cnt & Integer & Fwd\_Act\_Data\_Pkts & Integer \\ Flow\_Duration & Integer & Fwd\_LAT\_Max & Float & PSH\_Flag\_Cnt & Integer & Fwd\_Seg\_Size\_Min & Integer \\ Tot\_Fwd\_Pkts & Integer & Fwd\_LAT\_Min & Float & ACK\_Flag\_Cnt & Integer & Active\_Mean & Float \\ Tot\_Bwd\_Pkts & Integer & Bwd\_LAT\_Tot & Float & URG\_Flag\_Cnt & Integer & Active\_Std & Float \\ Tot\_Ten\_Fwd\_Pkts & Float & Bwd\_LAT\_Mean\_1 & Float & CWE\_Flag\_Count & Integer & Active\_Max & Float \\ Tot\_Len\_Bwd\_Pkts & Float & Bwd\_LAT\_Std & Float & ECL\_Flag\_Cnt & Integer & Active\_Min & Float \\ Fwd\_Pkt\_Len\_Max & Float & Bwd\_LAT\_Max & Float & Down/Up\_Ratio & Float & Idle\_Mean & Float \\ Fwd\_Pkt\_Len\_Min & Float & Bwd\_LAT\_Min & Float & Pkt\_Size\_Avg & Float & Idle\_Std & Float \\ Fwd\_Pkt\_Len\_Mean & Float & Fwd\_PSH\_Flags & Integer & Fwd\_Seg\_Size\_Avg & Float & Idle\_Max & Float \\ Fwd Pkt Len\_Std & Float & Bwd\_PSH\_Flags & Integer & Bwd\_Seg\_Size\_Avg & Float & Idle\_Min & Float \\ Bwd Pkt Len Max & Float & Fwd\_UR\_Pkts\_Plag & Integer & Fwd\_Byts/b\_Avg & Float & Label & Integer \\ Bwd Pkt Len Min & Float & Bwd\_URG\_Flags & Integer & Fwd\_Pkts/b\_Avg & Integer & Cat & Categorical \\ Bwd Pkt Len Mean & Float & Bwd\_Header\_Len & Integer & Fwd\_Bik\_Rate\_Avg & Integer & Sub\_Cat & Categorical \\ Bwd\_Pkt\_Len\_Std & Float & Fwd\_Pkts/s & Float & Bwd\_Byts/b\_Avg & Integer & \\ Flow\_Byts/s & Float & Bwd\_Pkts/s & Float & Bwd\_Pkts/b\_Avg & Integer & & \\ Flow\_Pkts/s & Float & Pkt\_Len\_Min & Float & Bwd\_Bik\_Rate\_Avg & Integer & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IoT-Botnet 2020 Dataset Feature set. where, \(I(U;V)\) is the MI between two random discrete variables, \(U\) and \(V\), such that \(U=\{u_{1},u_{2},\ldots,u_{k}\}\) and \(V=\{v_{1},v_{2},\ldots,v_{k}\}\) with \(k\) samples each. \(p(u,v)\) represents the joint probability mass function while \(p(u)\) and \(p(v)\) represent the marginal probabilities. In the context of feature selection from a dataset, the relevant features will contain useful information about the particular class. The MI is chosen in this study for finding the relevant features due to its ability to quantify the amount of information shared among the feature and a class. Figure 4 shows the best numerical features selected based on the MI value arranged in descending order. We selected the top \(80,\ 32,\ 16\), and \(8\) features based on the highest MI scores to find out the optimal set of useful and important features that can be used to train the DL model, with a negligible loss in the detection accuracy.