Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation

Jamming is a well-known reliability threat for mass-market wireless networks. With the rise of safety-critical applications this is likely to become a constraining issue in the future. Thus, the design of accurate jamming detection algorithms becomes important to react to ongoing jamming attacks. With respect to experimental work, jamming detection has been mainly studied for sensor networks. However, many safety-critical applications are also likely to run over 802.11-based networks where the proposed approaches do not carry over. In this paper we present a jamming detection approach for 802.11 networks. It uses metrics that are accessible through standard device drivers and performs detection via machine learning. While it allows for stand-alone operation, it also enables cooperative detection. We experimentally show that our approach achieves remarkably high detection rates in indoor and mobile outdoor scenarios even under challenging link conditions.