Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning

Shadi I. Abudalfa; Youssef S. Ezz-Eldeen; Sohil F. Barhoom; Albaraa S. Almughaiyer; Ameer A. Abu Mhady

doi:10.1007/978-981-99-7798-7_13

Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning

Shadi I. Abudalfa^*
, Youssef S. Ezz-Eldeen
, Sohil F. Barhoom
, Albaraa S. Almughaiyer
, Ameer A. Abu Mhady

^*Corresponding author for this work

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

1 Scopus citations

Abstract

In the recent period, companies and institutions have tended to use technology and websites to provide their services electronically. Therefore, these institutions need to protect their electronic services and know the types of received communications. In the same context, some hackers resort to using virtual private network (VPN) technology to carry out their attacks on company networks. In this work, we evaluate the performance of VPN traffic detection in networks through artificial intelligence. Detecting VPN traffic helps companies take appropriate action when classifying people who accessed their systems. Numerous experiment results are reported to show the performance of our work.

Original language	English
Title of host publication	Technical and Vocational Education and Training
Publisher	Springer
Pages	161-171
Number of pages	11
DOIs	https://doi.org/10.1007/978-981-99-7798-7_13
State	Published - 2024
Externally published	Yes

Publication series

Name	Technical and Vocational Education and Training
Volume	39
ISSN (Print)	1871-3041
ISSN (Electronic)	2213-221X

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keywords

Classification
Machine learning
Real time
Virtual private network

ASJC Scopus subject areas

Education

Access to Document

10.1007/978-981-99-7798-7_13

Cite this

Abudalfa, S. I., Ezz-Eldeen, Y. S., Barhoom, S. F., Almughaiyer, A. S., & Abu Mhady, A. A. (2024). Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning. In Technical and Vocational Education and Training (pp. 161-171). (Technical and Vocational Education and Training; Vol. 39). Springer. https://doi.org/10.1007/978-981-99-7798-7_13

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abstract = "In the recent period, companies and institutions have tended to use technology and websites to provide their services electronically. Therefore, these institutions need to protect their electronic services and know the types of received communications. In the same context, some hackers resort to using virtual private network (VPN) technology to carry out their attacks on company networks. In this work, we evaluate the performance of VPN traffic detection in networks through artificial intelligence. Detecting VPN traffic helps companies take appropriate action when classifying people who accessed their systems. Numerous experiment results are reported to show the performance of our work.",

keywords = "Classification, Machine learning, Real time, Virtual private network",

author = "Abudalfa, \{Shadi I.\} and Ezz-Eldeen, \{Youssef S.\} and Barhoom, \{Sohil F.\} and Almughaiyer, \{Albaraa S.\} and \{Abu Mhady\}, \{Ameer A.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.",

year = "2024",

doi = "10.1007/978-981-99-7798-7\_13",

language = "English",

series = "Technical and Vocational Education and Training",

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Abudalfa, SI, Ezz-Eldeen, YS, Barhoom, SF, Almughaiyer, AS & Abu Mhady, AA 2024, Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning. in Technical and Vocational Education and Training. Technical and Vocational Education and Training, vol. 39, Springer, pp. 161-171. https://doi.org/10.1007/978-981-99-7798-7_13

Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning. / Abudalfa, Shadi I.; Ezz-Eldeen, Youssef S.; Barhoom, Sohil F. et al.
Technical and Vocational Education and Training. Springer, 2024. p. 161-171 (Technical and Vocational Education and Training; Vol. 39).

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

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AU - Ezz-Eldeen, Youssef S.

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AU - Almughaiyer, Albaraa S.

AU - Abu Mhady, Ameer A.

PY - 2024

Y1 - 2024

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AB - In the recent period, companies and institutions have tended to use technology and websites to provide their services electronically. Therefore, these institutions need to protect their electronic services and know the types of received communications. In the same context, some hackers resort to using virtual private network (VPN) technology to carry out their attacks on company networks. In this work, we evaluate the performance of VPN traffic detection in networks through artificial intelligence. Detecting VPN traffic helps companies take appropriate action when classifying people who accessed their systems. Numerous experiment results are reported to show the performance of our work.

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KW - Machine learning

KW - Real time

KW - Virtual private network

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PB - Springer

ER -

Evaluating the Performance of Real-Time IP Traffic Classification in Virtual Private Networks Using Machine Learning

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this