Imbalanced Encrypted Traffic Classification Scheme Using Random Forest

Feng Zhang; Tao Shang; Jianwei Liu

doi:10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

Imbalanced Encrypted Traffic Classification Scheme Using Random Forest

Feng Zhang
, Tao Shang
, Jianwei Liu

School of Cyber Science and Technology

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Encrypted traffic classification techniques can identify different types of traffic for network security. The existing schemes seldom consider the imbalanced distribution of the traffic. In this paper, we propose an encrypted traffic classification scheme using random forest for imbalanced learning. Firstly, the weighted information gain is used to select the features which are beneficial to the minority class to filter redundant features. Then the hybrid sampling method is used to balance the number of the majority class and the minority class. Finally, the random forest is constructed for encrypted traffic classification. Experimental results show that the feature selection method can filter the redundant features of the traffic and the hybrid sampling method can effectively improve the classification ability. Compared with K-nearest neighbor and C4.5 decision tree algorithm, the proposed scheme can classify imbalanced encrypted traffic more effectively.

Original language	English
Title of host publication	Proceedings - IEEE Congress on Cybermatics
Subtitle of host publication	2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	837-842
Number of pages	6
ISBN (Electronic)	9781728176475
DOIs	https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142
State	Published - Nov 2020
Event	2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020 - Rhodes Island, Greece Duration: 2 Nov 2020 → 6 Nov 2020

Publication series

Name	Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

Conference

Conference	2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020
Country/Territory	Greece
City	Rhodes Island
Period	2/11/20 → 6/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Encrypted traffic classification
Feature selection
Hybrid sampling
Imbalanced learning
Random forest

Access to Document

10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

Cite this

Zhang, F., Shang, T., & Liu, J. (2020). Imbalanced Encrypted Traffic Classification Scheme Using Random Forest. In Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 (pp. 837-842). Article 9291688 (Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

Zhang, Feng ; Shang, Tao ; Liu, Jianwei. / Imbalanced Encrypted Traffic Classification Scheme Using Random Forest. Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 837-842 (Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020).

@inproceedings{8cd7437a964d44e89dea8c7cdbce1455,

title = "Imbalanced Encrypted Traffic Classification Scheme Using Random Forest",

abstract = "Encrypted traffic classification techniques can identify different types of traffic for network security. The existing schemes seldom consider the imbalanced distribution of the traffic. In this paper, we propose an encrypted traffic classification scheme using random forest for imbalanced learning. Firstly, the weighted information gain is used to select the features which are beneficial to the minority class to filter redundant features. Then the hybrid sampling method is used to balance the number of the majority class and the minority class. Finally, the random forest is constructed for encrypted traffic classification. Experimental results show that the feature selection method can filter the redundant features of the traffic and the hybrid sampling method can effectively improve the classification ability. Compared with K-nearest neighbor and C4.5 decision tree algorithm, the proposed scheme can classify imbalanced encrypted traffic more effectively.",

keywords = "Encrypted traffic classification, Feature selection, Hybrid sampling, Imbalanced learning, Random forest",

author = "Feng Zhang and Tao Shang and Jianwei Liu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020 ; Conference date: 02-11-2020 Through 06-11-2020",

year = "2020",

month = nov,

doi = "10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142",

language = "英语",

series = "Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "837--842",

booktitle = "Proceedings - IEEE Congress on Cybermatics",

address = "美国",

}

Zhang, F, Shang, T & Liu, J 2020, Imbalanced Encrypted Traffic Classification Scheme Using Random Forest. in Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020., 9291688, Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020, Institute of Electrical and Electronics Engineers Inc., pp. 837-842, 2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020, Rhodes Island, Greece, 2/11/20. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

Imbalanced Encrypted Traffic Classification Scheme Using Random Forest. / Zhang, Feng; Shang, Tao ; Liu, Jianwei.
Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 837-842 9291688 (Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Imbalanced Encrypted Traffic Classification Scheme Using Random Forest

AU - Zhang, Feng

AU - Shang, Tao

AU - Liu, Jianwei

PY - 2020/11

Y1 - 2020/11

N2 - Encrypted traffic classification techniques can identify different types of traffic for network security. The existing schemes seldom consider the imbalanced distribution of the traffic. In this paper, we propose an encrypted traffic classification scheme using random forest for imbalanced learning. Firstly, the weighted information gain is used to select the features which are beneficial to the minority class to filter redundant features. Then the hybrid sampling method is used to balance the number of the majority class and the minority class. Finally, the random forest is constructed for encrypted traffic classification. Experimental results show that the feature selection method can filter the redundant features of the traffic and the hybrid sampling method can effectively improve the classification ability. Compared with K-nearest neighbor and C4.5 decision tree algorithm, the proposed scheme can classify imbalanced encrypted traffic more effectively.

AB - Encrypted traffic classification techniques can identify different types of traffic for network security. The existing schemes seldom consider the imbalanced distribution of the traffic. In this paper, we propose an encrypted traffic classification scheme using random forest for imbalanced learning. Firstly, the weighted information gain is used to select the features which are beneficial to the minority class to filter redundant features. Then the hybrid sampling method is used to balance the number of the majority class and the minority class. Finally, the random forest is constructed for encrypted traffic classification. Experimental results show that the feature selection method can filter the redundant features of the traffic and the hybrid sampling method can effectively improve the classification ability. Compared with K-nearest neighbor and C4.5 decision tree algorithm, the proposed scheme can classify imbalanced encrypted traffic more effectively.

KW - Encrypted traffic classification

KW - Feature selection

KW - Hybrid sampling

KW - Imbalanced learning

KW - Random forest

UR - https://www.scopus.com/pages/publications/85099463961

U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

DO - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

M3 - 会议稿件

AN - SCOPUS:85099463961

T3 - Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

SP - 837

EP - 842

BT - Proceedings - IEEE Congress on Cybermatics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020

Y2 - 2 November 2020 through 6 November 2020

ER -

Zhang F, Shang T , Liu J. Imbalanced Encrypted Traffic Classification Scheme Using Random Forest. In Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 837-842. 9291688. (Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020). doi: 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00142

Imbalanced Encrypted Traffic Classification Scheme Using Random Forest

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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