Research Article
Identification of Group VPN Security Threats and Countermeasures using Machine Learning
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357845, author={S. Thangamani and C. Hemanth and C. Deepa and R. Poorani}, title={Identification of Group VPN Security Threats and Countermeasures using Machine Learning}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={vpn detection cybersecurity machine learning proxy identification network traffic analysis anomaly detection}, doi={10.4108/eai.28-4-2025.2357845} }- S. Thangamani
C. Hemanth
C. Deepa
R. Poorani
Year: 2025
Identification of Group VPN Security Threats and Countermeasures using Machine Learning
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357845
Abstract
As businesses increasingly rely on online services, cybercriminals use proxy networks and VPN services to undertake illegitimate activities in disguise. Group VPN? A group link VPN provides enhanced privacy and security but also presents security risks by allowing anonymous access and illicit activity. The solution presented in this paper is a feature-rich Group VPN discovery technique to thwart cyber-attacks and secure Welcome Network environments. The method combines machine learning algorithms and network traffic analysis for detection of VPN-related cyber-attacks. This research will help to enhance security systems to detect malicious VPN usage using supervised learning, feature engineering, and behavior analysis models. Experiments show superior performance on fraud prevention and the effectiveness of our real-time VPN detection.
