RASM A State Machine Methodology for RF Security Assessment

The speaker starts from a common frustration with radio work. Unlike a checklist-driven IoT assessment, RF work does not move cleanly from one step to the next. A capture may look distorted, or decoding may drop packets, and it is often unclear whether the cause was the capture setup, the environment, interference, or insufficient noise filtering. Retrying the whole chain at random wastes time, so the talk argues for a structured process that makes each decision explicit.

The main part of the talk introduces RASM, a work-in-progress tool that models an RF assessment as a state machine. A state can only be entered once its input conditions are met, such as having an identified target, a known frequency, and suitable hardware. The speaker walks through hardware selection as an example. A receive-only goal such as reverse engineering can work with a cheap RTL-SDR, while replaying a signal needs a transmitting radio like a HackRF. A wideband drone link may exceed what a HackRF can capture, pointing to a device such as a BladeRF. The tool asks about transmit needs, burst type, and whether the environment is dense, then recommends hardware and explains why alternatives fall short. Its loop design also lets a user return to an earlier state, such as recapturing, filtering, or amplifying, when a logged path shows missing data. A live demo was cut short by a network outage, so only screenshots were shown.

The speaker plans to release the tool as open source after the remaining states are built, and asked the community which functions would help their assessments. The practical takeaway is to treat RF work as a set of checkpoints with explicit entry conditions, so that a failed step points to a specific fix rather than a full restart.