Table 3.

Accuracy, F-measure, number of Trainable Parameters (#TP), and Training Time (Time) comparison of DL-based traffic classifiers when combining different mixes of modalities. 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. Training time is calculated by pre-training the individual modalities in parallel. The input types fed to each classifier are shown in Table 2.

Classifier	Joint-TC		App-TC		Activity-TC		#TP [k]	Time [min]
	Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]
bPAY	$42.34(\pm 1.12)$	$35.80(\pm 1.25)$	$76.27(\pm 0.84)$	$77.22(\pm 0.82)$	$53.57(\pm 0.70)$	$46.83(\pm 1.46)$	460	$22(\pm 3)$
bSEQ	$57.56(\pm 0.80)$	$51.37(\pm 1.12)$	$91.03(\pm 0.59)$	$92.36(\pm 0.53)$	$62.13(\pm 0.96)$	$59.12(\pm 0.91)$	706	$26(\pm 7)$
bContext	$66.44(\pm 1.58)$	$64.68(\pm 2.05)$	$81.12(\pm 1.53)$	$80.64(\pm 1.67)$	$78.91(\pm 0.91)$	$77.75(\pm 0.97)$	$\mathbf{99}$	$\mathbf{2}(\pm \mathbf{0})$
Mimetic-Enhanced	$67.12(\pm 1.14)$	$62.29(\pm 1.21)$	$98.54(\pm 0.21)$	$98.75(\pm 0.18)$	$67.94(\pm 1.13)$	$65.33(\pm 1.15)$	1235	$57(\pm 6)$
Mimetic-ConPay	$78.09(\pm 0.99)$	$76.94(\pm 0.98)$	$95.15(\pm 0.48)$	$95.01(\pm 0.52)$	$81.17(\pm 0.97)$	$80.36(\pm 0.92)$	628	$30(\pm 4)$
Mimetic-ConSeq	$81.15(\pm 0.84)$	$80.32(\pm 0.85)$	$97.39(\pm 0.42)$	$97.61(\pm 0.35)$	$83.02(\pm 0.74)$	$82.39(\pm 0.73)$	875	$33(\pm 4)$
Mimetic-All	$\mathbf{82.53}(\pm \mathbf{0.90})$	$\mathbf{81.04}(\pm \mathbf{1.02})$	$\mathbf{99.05}(\pm \mathbf{0.26})$	$\mathbf{99.17}(\pm \mathbf{0.22})$	$\mathbf{83.13}(\pm \mathbf{0.85})$	$\mathbf{82.51}(\pm \mathbf{0.81})$	1368	$56(\pm 7)$