Frame-by-frame Wi-Fi attack detection algorithm with scalable and modular machine-learning design
The popularity of Wi-Fi networks coupled with the intrinsic vulnerability of wireless interfaces has promoted the investigation and proposal of traffic analysis and anomaly detection algorithms targeted to that application. We propose a scalable and modular algorithm architecture to set up a lightweight classifier, able to detect malicious frames with high reliability, allowing a simple implementation and suitable for real-time operations.