Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction

Table 1

Accuracy comparison of DETD and eight current encryption traffic intrusion detection algorithms.

Algorithms	Description	Accuracy (%)
Yang et al. [17]	Autoencoder	96.190
	CNN	97.901
Zeng et al. [18]	1D-CNN + L1 regularization	99.851
	LSTM + L1 regularization	99.222
	SAE + L1 regularization	98.741
Anderson and Mcgrew [10]	SPLT + BD + TLS + HTTP + DNS	99.993
	SPLT + BD + TLS + HTTP	99.983
	SPLT + BD + TLS + DNS	99.988
	HTTP + DNS	99.985
Anderson and Mcgrew [11]	Meta + SPLT + BD + TLS + SS	99.601
	Meta + SPLT + BD + TLS	99.610
	TLS	98.202
	Meta + SPLT + BD	98.900
Anderson et al. [12]	Linear regression	99.281
	L1-logistic regression	98.972
	Random forest	99.990
	MLP	99.542
Odiathevar et al. [30]	AE + MW + IGMM	97.435
Kim et al. [31]	RVAE	97.534
Wang et al. [32]	Model collaboration	94.353
Our proposed DETD	PASE + RF + adaboost/gradient boosting	99.994
	PASE + L1_R + adaboosting	99.998

The bolded values represent the best-performing result.