An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model

Weijie Han; Jingfeng Xue; Fuquan Zhang; Yingfeng Zhang

doi:10.1007/978-3-030-62223-7_3

An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model

Weijie Han^*, Jingfeng Xue, Fuquan Zhang, Yingfeng Zhang

^*Corresponding author for this work

School of Computer Science and Technology

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

Abstract

Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.

Original language	English
Title of host publication	Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
Editors	Xiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	26-34
Number of pages	9
ISBN (Print)	9783030622220
DOIs	https://doi.org/10.1007/978-3-030-62223-7_3
Publication status	Published - 2020
Event	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 - Guangzhou, China Duration: 8 Oct 2020 → 10 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12486 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020
Country/Territory	China
City	Guangzhou
Period	8/10/20 → 10/10/20

Keywords

Anomalous traffic detection
Network anomalous behavior
Private network
Self-learning model

Access to Document

10.1007/978-3-030-62223-7_3

Cite this

Han, W., Xue, J., Zhang, F., & Zhang, Y. (2020). An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. In X. Chen, H. Yan, Q. Yan, & X. Zhang (Eds.), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings (pp. 26-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12486 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62223-7_3

Han, Weijie ; Xue, Jingfeng ; Zhang, Fuquan et al. / An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. editor / Xiaofeng Chen ; Hongyang Yan ; Qiben Yan ; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 26-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{80c3b7fe14c34552b31197cbbc8b9972,

title = "An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model",

abstract = "Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.",

keywords = "Anomalous traffic detection, Network anomalous behavior, Private network, Self-learning model",

author = "Weijie Han and Jingfeng Xue and Fuquan Zhang and Yingfeng Zhang",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 ; Conference date: 08-10-2020 Through 10-10-2020",

year = "2020",

doi = "10.1007/978-3-030-62223-7_3",

language = "English",

isbn = "9783030622220",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "26--34",

editor = "Xiaofeng Chen and Hongyang Yan and Qiben Yan and Xiangliang Zhang",

booktitle = "Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings",

address = "Germany",

}

Han, W, Xue, J, Zhang, F & Zhang, Y 2020, An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. in X Chen, H Yan, Q Yan & X Zhang (eds), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12486 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 26-34, 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020, Guangzhou, China, 8/10/20. https://doi.org/10.1007/978-3-030-62223-7_3

An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. / Han, Weijie; Xue, Jingfeng; Zhang, Fuquan et al.
Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. ed. / Xiaofeng Chen; Hongyang Yan; Qiben Yan; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. p. 26-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12486 LNCS).

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

TY - GEN

T1 - An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model

AU - Han, Weijie

AU - Xue, Jingfeng

AU - Zhang, Fuquan

AU - Zhang, Yingfeng

PY - 2020

Y1 - 2020

N2 - Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.

AB - Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.

KW - Anomalous traffic detection

KW - Network anomalous behavior

KW - Private network

KW - Self-learning model

UR - http://www.scopus.com/inward/record.url?scp=85097174697&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-62223-7_3

DO - 10.1007/978-3-030-62223-7_3

M3 - Conference contribution

AN - SCOPUS:85097174697

SN - 9783030622220

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 26

EP - 34

BT - Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings

A2 - Chen, Xiaofeng

A2 - Yan, Hongyang

A2 - Yan, Qiben

A2 - Zhang, Xiangliang

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

Y2 - 8 October 2020 through 10 October 2020

ER -

Han W, Xue J, Zhang F, Zhang Y. An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model. In Chen X, Yan H, Yan Q, Zhang X, editors, Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 26-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-62223-7_3

An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this