AI-Enhanced VPN Security Framework: Integrating Open-Source Threat Intelligence and Machine Learning to Secure Digital Networks

Abstract

In today's digital age, ensuring network privacy and integrity is of utmost importance. To address this, our work proposed an advanced VPN security framework that integrates open-source threat intelligence and machine learning (ML) to enhance cyber defences. By combining Wazuh for threat detection and analysis, and pfsense for firewall capabilities, with state-of-the-art ML algorithms, we present a robust VPN security solution to the challenges presented by the evolving landscape of cyber threats, representing a significant advancement in securing digital networks. This framework is strengthened by the integration of four ML algorithms— Gradient Boosted Trees (GBT), Random Forest (RF), K-Nearest Neighbors (KNN), and Dense Deep Learning (DDL)— chosen for their classification efficacy and their ability to process complex security data, thereby improving the efficiency and accuracy of threat detection. Results indicated significant improvements in threat detection accuracy following the integration of ML algorithms. The Random Forest (RF) algorithm, in particular, stood out for its exceptional accuracy and ability to handle various threat scenarios, showcasing its efficacy in identifying sophisticated cyber threats through network traffic pattern analysis. Further performance benchmarking confirmed the feasibility of deploying the advanced VPN security framework, demonstrating minimal impact on network latency and throughput.