# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 6 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Dataset Size** & \
\begin{tabular}{c} **Classification** \\ **Problem** \\ \end{tabular} & **Method** &
\begin{tabular}{c} **Feature Selection** \\ **Strategy** \\ \end{tabular} & **Performance Index** & **Performance (\%)** \\ \hline
[10] & Entire AWID dataset &
\begin{tabular}{c} Both using \\ macro-classes and \\ single attack labels \\ \end{tabular} & Random Tree J48 & Manual & Accuracy & 96.1982 \\ \hline
[11] & Entire AWID dataset & \begin{tabular}{c} Both using \\ macro-classes and \\ single attack labels \\ \end{tabular} & \
\begin{tabular}{c} Particle Swarm \\ Optimization-based \\ \end{tabular} &
\begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Accuracy & 77.8 \\ \hline
[48] & \
\begin{tabular}{c} AWID subset \\ (\(\sim\)2.4 millions patterns) \\ \end{tabular} & 4 macro-classes &
\begin{tabular}{c} Deep Learning Stacked \\ Autoencoders \\ \end{tabular} & Automatic & Accuracy & 98.6688 \\ \hline
[43] & \begin{tabular}{c} AWID subset \\ (\(\sim\)1.3 millions patterns) \\ \end{tabular} & 4 macro-classes & SVM & \
\begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Accuracy &
\begin{tabular}{c} 89-99 (depending \\ on class) \\ \end{tabular} \\ \hline
[14] &
\begin{tabular}{c} Modified subset of the \\ AWID dataset \\ \end{tabular} & 7 attack classes & HMM and KDE & Automatic & Precision & 92.28 \\ \hline
[44] & \begin{tabular}{c} AWID subset \\ (\(\sim\)2.3 millions patterns) \\ \end{tabular} & 4 macro-classes & \
\begin{tabular}{c} Bagging, Random \\ Forests ExtraTrees and \\ XGBoost \\ \end{tabular} &
\begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Accuracy & 99.1 \\ \hline
[1] & \begin{tabular}{c} AWID subset \\ (\(\sim\)84 millions patterns) \\ \end{tabular} & 14 attack classes & \begin{tabular}{c} BSA and K-NN driven \\ by GA \\ \end{tabular} & \
\begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Accuracy &
\begin{tabular}{c} 92.446 (false alarm \\ rate 12.1) \\ \end{tabular} \\ \hline
[1] & \begin{tabular}{c} AWID subset \\ (\(\sim\)84 millions patterns) \\ \end{tabular} & 14 attack classes & \
\begin{tabular}{c} BSA and Min-Max \\ Neural Network \\ \end{tabular} & Manual & Accuracy &
\begin{tabular}{c} 89-90 (false alarm \\ rate 11.6) \\ \end{tabular} \\ \hline
[3] & \begin{tabular}{c} AWID subset \\ (\(\sim\)565,000 patterns) \\ \end{tabular} & 6 attack classes & \begin{tabular}{c} Granular \\ Compounding-based \\ approach driven by GA \\ \end{tabular} & \begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Reliability & \begin{tabular}{c} 89.3 (\(L_{\infty}=[6,7]\), \(B=10\)) \\ \end{tabular} \\ \hline This work & \begin{tabular}{c} AWID subset \\ (\(\sim\)565,000 patterns) \\ \end{tabular} & 14 attack classes & \begin{tabular}{c} Granular \\ Compounding-based \\ approach and GNN \\ driven by GA \\ \end{tabular} & \begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Accuracy & 93.9 (\(B=50\)) \\ \hline This work & \begin{tabular}{c} AWID subset \\ (\(\sim\)565,000 patterns) \\ \end{tabular} & 14 attack classes & \
\begin{tabular}{c} Granular \\ Compounding-based \\ approach and GNN \\ driven by GA \\ \end{tabular} &
\begin{tabular}{c} Both manual and \\ automatic \\ \end{tabular} & Jaccard Similarity & 73.2 (\(B=20\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison with related state-of-the-art works.