AI-Driven VPN Threat Detection and Preventive Mechanism

The widespread adoption of remote work has significantly established the Virtual Private Networks (VPNs) as a critical security tool since data remains vulnerable to Man-in-theMiddle (MITM) attacks at the local network layer.Hence, the data is being intercepted and manipulated by adversaries before it enters the secure tunnel.However, this research addresses this vulnerability by designing, implementing, and evaluating a real-time hybrid detection and prevention system for MITM attacks on VPN connections.Similarly, a controlled laboratory environment was used to simulate ARP spoofingbased MITM attacks against a commercial VPN client.Thus, the network traffic was analyzed to identify distinctive signatures of interception and integrated into a Pythonbased detection engine using Scapy.The signature-based and rule-based techniques were combined to monitor ARP traffic for inconsistencies such as IP-MAC binding violations and Unsolicited ARP replies.Interestingly, a 98.7% detection rate was achieved with an average response latency of 340 milliseconds.An automated mitigation module successfully neutralized 96.8% of attacks by broadcasting corrective ARP packets to restore legitimate network mappings.