Fortifying Public Area Networks: Leveraging Ensemble Learning to Combat VPN Malicious Transmissions
Aadil Khan, Deepali Gupta, Monica Dutta · 2024
In this article, a novel cybersecurity framework is created by using a variety of machine learning algorithms. The objective of the work is to seek and stop malicious communications from entering network infrastructures, especially in public places (which are prone to digital attacks). Issues arise as virtual private networks (VPNs) are more and more used on public networks. The purpose of this research is to track vulnerabilities that may exist in the virtual private network setup of networks and to see which directions people tend to take. This enables the computer to quickly discern deviations from the norm so that false information can be found and remedied in time. The method improves digital security by addressing flaws in VPN, thereby reducing the risk of cyber-attacks, and stopping illegal abuse. The results of this experiment show that the algorithm proposed in the article can find and prevent VPN connections and other possible incursions of harmful information. That ensemble learning was both precise and accurate, makes the effectiveness of the algorithm more credible. This research also improves security measures that can be taken to protect public networks from cyber-attacks.