Abstract: Recent abuse of Virtual Private Networks (VPNs) has introduced significant challenges in network monitoring for enterprises, particularly with the rise of encrypted traffic that obscures legitimate from malicious activities. Malicious traffic is increasingly routed through VPNs, making it difficult to detect unauthorized data transfers. Traditional traffic analysis tools are ineffective at identifying encrypted VPN traffic, leaving networks vulnerable to attacks. This paper presents a machine learning-based framework designed to detect encrypted VPN traffic within enterprise networks. By analyzing network flow data, the framework extracts relevant features to train machine learning models that identify anomalous traffic patterns, which often indicate malicious activity. The system incorporates both supervised and unsupervised learning algorithms for the detection and classification of VPN traffic, providing an advanced method for monitoring encrypted communications. Experimental results demonstrate that machine learning models can significantly improve the detection of VPN traffic, offering a scalable, non-intrusive solution for securing networks. The framework allows organizations to maintain high security levels without compromising user privacy or decrypting encrypted communications. This system adds to the growing collection of effective solutions aimed at addressing the challenge of securing networks while managing VPN traffic.
Keywords: Machine Learning, VPN Traffic, Detection, Network Security, Encrypted Communications, Anomaly Detection
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