Virtual Private Network: Survey and Research Challenges

Abstract: The Virtual Private Network (VPN) is a rapidly developing technology that has a crucial function in guaranteeing the secure transfer of data. A virtual linkage is created among geographically distant individuals and networks that are linked through a common or public network, such as the Internet. Despite the fact that data is transmitted via a public network, a Virtual Private Network (VPN) signifies that the data is conveyed through a secure and private connection. Virtual Private Network (VPN) technology enables secure communication between two hosts by creating virtual tunnels. Upon the establishment of a tunnel, it becomes possible to transmit data between the two servers. This paper provides a comprehensive analysis of Virtual Private Networks (VPNs), including their historical background, architectural design, employed protocols, and identified vulner- abilities. A Virtual Private Network (VPN) is implemented to secure the private network by utilising encryption and other security measures, thereby ensuring that solely authorised users are granted access to the system and preventing data interception. This survey comprehensively covers the topics of Virtual Private Networks (VPNs), their protocols, and the security measures implemented within VPNs. The present article has conducted an overview of the diverse protocols employed in the domain of Virtual Private Networking.

VPN, Security Protocols, Tunneling, Remote Access, Tunnel Mode, Transport Mode

1. Ikea, S., & Erten, Y. M., ”Throughput Analysis of UDP and VPN Traffic in 802.11g Networks,” Lecture Notes in Computer Science, (2006), pp.474–482. [Google Scholar] [Crossref]

2. Antonello, R., Fernandes, S., Kamienski, C., Sadok, D., Kelner, J., Go´dor, I., . . . Westholm, T., ”Deep packet inspection tools and techniques in commodity platforms: Challenges and Trends,” Journal of Network and Com- puter Applications, (2012), 35(6), pp.1863–1878. DOI: 10.1016/j.jnca.2012.07.010. [Google Scholar] [Crossref]

3. S. Feghhi and D. J. Leith, ”A Web Traffic Analysis Attack Using Only Timing Information,” in IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1747- 1759, Aug. 2016, DOI: 10.1109/TIFS.2016.2551203. [Google Scholar] [Crossref]

4. S. Bagui, X. Fang, E. Kalaimannan, S. C. Bagui & [Google Scholar] [Crossref]

5. J. Sheehan, ”Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features” in Journal of Cyber Security Technology, (2017), DOI: 10.1080/23742917.2017.1321891. [Google Scholar] [Crossref]

6. L. Guo, Q.Wu, S. Liu, M. Duan, H. Li, J. Sun, ”Deep learning-based real-time VPN encrypted traffic identification methods,” in Journal of Real-Time Image Processing, (2019), DOI: 10.1007/s11554-019-00930-6 [Google Scholar] [Crossref]

7. Zhang, Y., Chen, J., Chen, K., Xu, R., Teh, J., & Zhang, S., ”Network traffic identification of several open-source secure proxy protocols,” International Journal of Network Manage- ment. (2019) DOI:10.1002/nem.2090. [Google Scholar] [Crossref]

8. Miller, S., Curran, K., & Lunney, T., ”Detection of VPN Traffic Using Machine Learning.” International Journal of Wireless Networks and Broadband Technologies, (2020), 9(2), pp.60–80. DOI:10.4018/ijwnbt.2020070104. [Google Scholar] [Crossref]

9. P. Gao, G. Li, Y. Shi, and Y. Wang, ”VPN Traf- fic Classification Based on Payload Length Sequence,” 2020 International Conference on Networking and Net- work Applications (NaNA), 2020, pp. 241-247, DOI: 10.1109/NaNA51271.2020.00048. [Google Scholar] [Crossref]

10. R. Nigmatullin, A. Ivchenko, and S. Dorokhin, ”Differ- entiation of Sliding Rescaled Ranges: New Approach to En- crypted and VPN Traffic Detection,” 2020 International Con- ference Engineering and Telecommunication (En&T), 2020, pp. 1-5, DOI: 10.1109/EnT50437.2020.9431285. [Google Scholar] [Crossref]

11. Ji, S.-Y., Jeong, B.-K., & Jeong, D. H., ”Evaluating visu- alization approaches to detect abnormal activities in network traffic data,” International Journal of Information Security, (2020), 20(3), pp. 331–345. DOI:10.1007/s10207-020-00504-9. [Google Scholar] [Crossref]

12. V. A. Muliukha, L. U. Laboshin, A. A. Lukashin, and N. V. Nashivochnikov, ”Analysis and Classification of Encrypted Network Traffic Using Machine Learning,” 2020 XXIII International Conference on Soft Comput- ing and Measurements (SCM), 2020, pp. 194-197, DOI: 10.1109/SCM50615.2020.9198811. [Google Scholar] [Crossref]

13. F. U. Islam, G. Liu, J. Zhai, and W. Liu, ”VoIP Traffic Detection in Tunneled and Anonymous Networks Using Deep Learning,” in IEEE Access, vol. 9, pp. 59783-59799, 2021, DOI: 10.1109/ACCESS.2021.3073967. [Google Scholar] [Crossref]

14. Lu, B., Luktarhan, N., Ding, C., & Zhang, W., ”ICLSTM: Encrypted Traffic Service Identification Based on Inception- LSTM Neural Network,” Symmetry, (2021), 13(6), 1080. pp.1- DOI:10.3390/sym13061080. [Google Scholar] [Crossref]

15. Lee, J., & Lee, H., ”An SSH predictive model using machine learning with web proxy session logs,” International Journal of Information Security, (2021). DOI:10.1007/s10207- 021-00555-6. [Google Scholar] [Crossref]

16. A. A. Jaha, F. B. Shatwan and M. Ashibani, ”Proper Virtual Private Network (VPN) Solution,” 2008 The SecondInternational Conference on Next Generation Mobile Appli- cations, Services, and Technologies, 2008, pp. 309-314, doi: 10.1109/NGMAST.2008.18. [Google Scholar] [Crossref]

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