An Energy-Efficient ESP32 IoT System for Real-Time Detection of WiFi Deatuhentication Attacks

Abstract

WiFi deauthentication attacks pose a serious threat to users on public WiFi networks by forcibly disconnecting them from access points, often as a prelude to man-in-the-middle exploits. To counter this threat, we developed an energy-efficient ESP32-based IoT system that monitors WiFi traffic in real time to identify deauthentication attack patterns. The device captures deauthentication frames in monitor mode and immediately notifies users through on-device audible/visual alarms (buzzer, LED/OLED) and digital channels (MQTT dashboard and Telegram bot). Experimental evaluation under moderate and high attack scenarios demonstrated robust performance: detection accuracy remained above 97% even under heavy attack traffic (97.8% at peak intensity). Furthermore, the system’s duty-cycled design limited average power consumption to ~79 mA (~30% lower than continuous monitoring) and achieved a rapid notification latency of ~270 ms, confirming real-time responsiveness. By combining physical indicators with online alerts, the system effectively warns users and improves public digital security literacy by making cyber threats immediately visible and understandable. Overall, these results establish the proposed system as a low-power, real-time attack detection solution that enhances WiFi network security and user awareness.