Improving VPN Security Using FFNN-Based Cybersecurity Models

The rapid growth of modern networks and the increasing volume of encrypted traffic have significantly intensified cybersecurity threats, particularly within Virtual Private Network (VPN) environments. It is suggested that an intelligent intrusion detection and prevention system using an intelligent Feedforward Neural Network (FFNN) can be proposed to improve the security of VPNs and the performance of the network is acceptable. The labeled network traffic data is used to test the proposed method whereby the traffic flows of the HTTP are examined to determine the throughput and the latency performance. Training the FFNN model is done to learn and classify malicious and benign traffic patterns based on supervised learning and flow-level characteristics of encrypted VPN traffic. It is experimentally proven that VPN tunneling will add more delay and performance overhead to an application, nevertheless, the suggested FFNN-based security model enables proactive prediction of a threat and prompt reaction with a 98% detection rate and the low processing time. The findings support the claim that using lightweight deep learning models in VPN architectures can be an effective way of enhancing cybersecurity resilience without adversely affecting network efficiency.