  Providing security guarantees for embedded devices with limited interface
capabilities is an increasingly crucial task. Although these devices don't have
traditional interfaces, they still generate unintentional electromagnetic
signals that correlate with the instructions being executed. By collecting
these traces using our methodology and leveraging a random forest algorithm to
develop a machine learning model, we built an EM side channel based instruction
level disassembler. The disassembler was tested on an Arduino UNO board,
yielding an accuracy of 88.69% instruction recognition for traces from twelve
instructions captured at a single location in the device; this is an
improvement compared to the 75.6% (for twenty instructions) reported in
previous similar work.
