Unified System for Detecting and Mitigating Evil Twin Attacks on Industrial Wi-Fi Networks with IoT/IIoT

Abstract

Wi-Fi network security in industrial environments has become a critical challenge in the era of digital transformation, especially with the widespread adoption of IoT and IIoT devices. The Evil Twin attack, which creates fraudulent access points identical to legitimate ones, poses a serious threat to confidentiality and availability in industrial systems. This study proposes a Unified Detection and Mitigation System (UDMS) that integrates RADIUS-based robust authentication, continuous monitoring using Snort intrusion detection system, and centralized access management via Active Directory. Tested in five progressive scenarios, the system reduced attack success rates from 87% (WPA2-PSK) to just 2%, while maintaining latency below 400ms, with low false positive (6%) and false negative (3%) rates. The research provides a practical framework for securing industrial Wi-Fi networks without compromising operational performance.