Table 5.

Accuracy and F-measure comparison of DL-based Mimetic-All against ML-based traffic classifiers when using all input types (i.e. PAY, SEQ, and Context). Models are trained using App$\times$Act class labels. Results are in the format $avg.(\pm std.)$ obtained over 10-folds. The best result per metric (column) is highlighted in boldface.

Classifier	DL	Joint-TC		App-TC		Act-TC
		Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]
DT	$○$	$67.96(\pm 1.15)$	$66.05(\pm 1.36)$	$95.91(\pm 0.62)$	$96.35(\pm 0.56)$	$70.12(\pm 1.15)$	$68.89(\pm 1.34)$
RF	$○$	$80.57(\pm 0.74)$	$77.94(\pm 0.86)$	$98.92(\pm 0.14)$	$99.14(\pm 0.12)$	$81.33(\pm 0.79)$	$80.41(\pm 0.82)$
Mimetic-All	●	$\mathbf{82.53}(\pm \mathbf{0.90})$	$\mathbf{81.04}(\pm \mathbf{1.02})$	$\mathbf{99.05}(\pm \mathbf{0.26})$	$\mathbf{99.17}(\pm \mathbf{0.22})$	$\mathbf{83.13}(\pm \mathbf{0.85})$	$\mathbf{82.51}(\pm \mathbf{0.81})$