Using Ensemble Learning for Securing Public Area Networks from VPN Malicious Transmissions

In this paper, a new paradigm for network security is developed via the use of ensemble learning. The primary objective of this study is to identify and stop the involvement of malicious communication into network configurations, especially in public areas that are vulnerable to cyber-attacks. The use of sophisticated machine learning in this research improves the safety and protection of networks. The increasing usage of virtual private networks (VPNs) on public networks is causing problems. Virtual private networks (VPNs) improve connection speeds and privacy, but they may also make it easier for criminals to get unwanted access. The purpose of this research is to enhance network safety by identifying potential vulnerabilities and analyzing trends in virtual private network (VPN) use. The complicated network traffic patterns are analyzed using a combination of several different machine learning algorithms. This enables the computer to swiftly detect deviations from norms, allowing for the identification of fraudulent data and the facilitation of proactive response. By addressing vulnerabilities in VPNs, the strategy enhances digital security, lowers the risk of cyberattacks, and stops unlawful activities. The results of this experiment demonstrate that the proposed algorithm is able to identify and prevent VPN connections and other potentially harmful data infiltrations. The effectiveness of ensemble learning was shown by its high level of accuracy. This research improves network security measures that may be used to defend public networks from cyberattacks.