Retracted : Detection Technique to trace IP behind VPN/Proxy using Machine Learning

##plugins.themes.academic_pro.article.sidebar##

Published Feb 15, 2023
https://doi.org/10.47164/ijngc.v14i1.1006
Download
Requires Subscription pdf
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 14, Special Issue 1, February 2023

##plugins.themes.academic_pro.article.main##

Devishree Naidu
Shri Ramdeobaba College of Engineering and Management ,Nagpur
Madhav Jha
Shri Ramdeobaba College of Engineering and Management ,Nagpur

Abstract

Cybercriminals use a variation of techniques to fleece their digital footprints, that creates a barrier for law enforcement agencies to impossibly catch and prosecute them. With the known universal truth that whenever a machine tries to connect in adversely to target system. The victim’s machine can see only requests coming from the “proxy” or the VPN server. Now as VPN hides IP addresses it leads the network to be redirected through some special configured remote server which are run by a VPN host. As its consequences, the user’s digital footprint is hidden. the footprint of a VPN server is received by the receiver. This challenges the entire organization or one’s personal system to be in risk. One such solution to the problem is to design “Honeypot system” that will trace an IP address running behind VPN/proxy servers. The machine learning algorithm will able to trace the actual IP address with ISP details. The paper discusses a detection mechanism that will dupe the attackers. Showing inability in locating and identifying real honeypot file. The methods were tested on various platforms and technique outperform in detecting attacker’s system smartly using machine learning.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Naidu, D., & Jha, M. (2023). Retracted : Detection Technique to trace IP behind VPN/Proxy using Machine Learning. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1006
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Jiang, K. and Zheng, H. 2020. Design and implementation of a machine learning enhanced web honeypot system. In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 957–961. DOI: https://doi.org/10.1109/CISP-BMEI51763.2020.9263640
  2. Leaden, G., Zimmermann, M., DeCusatis, C., and Labouseur, A. G. 2017. An API honeypot for ddos and xss analysis. In 2017 IEEE MIT undergraduate Research Technology Conference (URTC). 1–4. DOI: https://doi.org/10.1109/URTC.2017.8284180
  3. Miller, S., Curran, K., and Lunney, T. 2021. Detection of anonymising proxies using machine learning. International Journal of Digital Crime and Forensics (IJDCF) 13, 6, 1–17. DOI: https://doi.org/10.4018/IJDCF.286756
  4. Kapoor, A., Gupta, A., Gupta, R., Tanwar, S., Sharma, G. and Davidson, I.E., 2021. Ransomware detection, avoidance, and mitigation scheme: a review and future directions. Sustainability, 14(1), p.8. DOI: https://doi.org/10.3390/su14010008
  5. Pannu, M., Gill, B., Bird, R., Yang, K., and Farrel, B. 2016. Exploring proxy detection methodology. In 2016 IEEE International Conference on Cybercrime and Computer Forensic (ICCCF). 1–6. DOI: https://doi.org/10.1109/ICCCF.2016.7740438
  6. Smart, P., Thanammal, K., and Sujatha, S. 2022. A technique for spatial data classification using random forest based correlation. International Journal of Next-Generation Computing. DOI: https://doi.org/10.47164/ijngc.v13i1.385