Tor Anonymous Traffic Identification Method Based on Weighted Stacking Ensemble Learning

doi:10.3969/j.issn.1671-1122.2021.12.016

Abstract

Abstract:

The Tor network is often utilized by criminals to engage in various illegal activities, so it is important to identify the tor traffic efficiently for network supervision and fighting against crime. In this paper, based on the integrated learning idea, the weighted stacking model for tor traffic identification was proposed to solve the problem of sparse tor traffic and low recognition accuracy in real environment. Based on the data flow, time correlation characteristics of the flow were extracted, and the first 14 features of the information gain were calculated to form the input data set. KNN, SVM and XGBoost were weighted differently and used as base learners. XGBoost was used as the meta learners to construct two-layer stacking model. Compared with 10 algorithms on the open data set, the experimental results show that the recognition model proposed in this paper is superior to most algorithms in accuracy and has a lower missed rate, which is more in line with the target of tor traffic recognition in real network environment.

Key words: anonymous network, Tor, unbalanced data, Stacking

CLC Number:

TP309

WANG Xirui, LU Tianliang, ZHANG Jianling, DING Meng. Tor Anonymous Traffic Identification Method Based on Weighted Stacking Ensemble Learning[J]. Netinfo Security, 2021, 21(12): 118-125.

Figures/Tables 12

References 21

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特征	特征描述	重要性
Bwd IAT Std	反向间隔时间标准差	138.9
Bwd IAT Max	反向间隔时间最大值	36.3
Flow Bytes/s	流字节/s	28.3
Flow Duration	流持续时间	18.1
Bwd IAT Min	反向间隔时间最小值	13.8
Flow IAT Min	流间隔时间最小值	12.0
Flow IAT Std	流间隔时间标准差	11.6
Flow IAT Max	流间隔时间最大值	11.2
Fwd IAT Mean	正向间隔时间均值	10.9
Bwd IAT Mean	反向间隔时间均值	9.5
Fwd IAT Std	正向间隔时间标准差	9.5
Fwd IAT Min	正向间隔时间最小值	9.5
Flow Packets/s	流包数/s	6.8
Fwd IAT Max	正向间隔时间最大值	6.0

	预测正例	预测负例
真实正例	TP（True Positive）	FN（False Negative）
真实负例	FP（False Positive）	TN（True Negative）

	KNN	SVM	RF	XGBoost	LightGBM
KNN	1	0.59	0.86	0.84	0.86
SVM	0.59	1	0.60	0.59	0.59
RF	0.86	0.60	1	0.95	0.95
XGBoost	0.84	0.59	0.95	1	0.97
LightGBM	0.86	0.59	0.95	0.97	1

	Precision	Recall	F-measure	AUC
LR	0.971	0.960	0.965	0.978
RF	0.969	0.966	0.967	0.981
XGBoost	0.978	0.975	0.976	0.986

	Precision	Recall	F-measure	AUC
标准Stacking模型	0.967	0.961	0.963	0.978
仅KNN加权	0.964	0.967	0.966	0.981
仅SVM加权	0.970	0.966	0.968	0.982
仅XGBoost加权	0.970	0.968	0.969	0.982
加权Stacking模型（本文方案）	0.978	0.975	0.976	0.986