Real-time jammer localization inside a building based on a machine learning approach

Jamming attacks create harmful disruptions in wireless communication systems. Timely and precise localization of a jammer is vital in critical indoor settings, such as airports or hospitals. Inside our laboratory building, machine learning models are built to predict a commercial jammer location based on a antenna network which measures electromagnetic activity. Hence, the proposed methodology in this paper is able to predict in quasi real-time the room where the jammer is activated with a very good accuracy.