Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield
Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Alberto Huertas Celdrán, Robin Wassink, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller · 2023
Resource-constrained spectrum sensors from the Internet of Battlefield Things (IoBT) monitor the frequency spectrum to communicate over unoccupied bands, intercept enemy transmissions, or decode valuable information. However, they are also susceptible to Spectrum Sensing Data Falsification (SSDF) attacks manipulating the sensing data and impacting the previous services. Detection systems based on fingerprinting and machine learning have shown promising performance while detecting existing SSDF attacks. However, novel attacks reducing their impact on sensors behaviors have not been analyzed yet. Thus, this work redesigns and reimplements seven SSDF attacks by modifying spectrum data in the sensor memory instead of at later stages in the file system. Several experiments with current intelligent detection systems demonstrated that more effort is needed from the defensive perspective since the new SSDF attacks evade their detection. In this sense, literature-based detection methods achieve less than a 0.50 True Positive Rate when detecting the new implementations of the attacks.