UAV Spectrum Sensing against SSDF Attacks under Transmission Errors

Unmanned aerial vehicles (UAVs) spectrum sensing is susceptible to Spectrum Sensing Data Falsification (SSDF) attacks, which pose a threat to its data security. However, most existing SSDF defense strategies overlook transmission errors between secondary users (SUs) and fusion center (FC). Hence, this study introduces a reputation-based defense method against SSDF attacks for UAV spectrum sensing, accounting for potential transmission errors between UAVs and FC. Based on the probability of each UAV reporting correct sensing results, we assign a reputation value to each UAV and exclude those with a value lower than or equal to the threshold from participating in collaborative spectrum sensing. To enhance precision, we calculate the individual detection probability for each UAV. By evaluating performance under the same sensing environment and considering transmission errors, we compared our method with an error-free SSDF defense approach that ignores these errors. Simulation results confirm that calculating the individual detection probability yields more precise results, and our proposed method is more effective in distinguishing legitimate users.