Fake base station (FBS) crime is a type of wireless communication crime that has appeared recently. The key to enforcing the laws on regulating FBS based crime is not only to arrest but also to convict criminals effectively. Much work on FBS discovering, localization, and tracking can assist the arresting, but the problem of collecting evidence accurately to support a proper conviction has not been addressed yet.

To fill in the gap of enforcing the laws on FBS crimes, we design FBSleuth, an FBS crime forensics framework utilizing "radio frequency (RF) fingerprints'', e.g., the unique characteristics of the FBS transmitters embedded in the electromagnetic signals. Essentially, such fingerprints stem from the imperfections in hardware manufacturing and thus represent a consistent bond between an individual FBS device and its committed crime. We model the RF fingerprint from the subtle variance of the modulation errors, instantaneous frequency, and phases of the RF signals. Our validation of FBSleuth on six FBSes from four cities over more than 5 months shows that FBSleuth can achieve over 99% precision, 96.4% recall, and 97.94% F1 score in a dynamic wild environment.