With the widespread use of smart devices, device authentication has received much attention. One popular method for device authentication is to utilize internally measured device fingerprints, such as device ID, software or hardware-based characteristics. In this article, we propose
DeMiCPU
, a stimulation-response-based device fingerprinting technique that relies on externally measured information, i.e., magnetic induction (MI) signals emitted from the CPU module that consists of the CPU chip and its affiliated power-supply circuits. The key insight of
DeMiCPU
is that hardware discrepancies essentially exist among CPU modules and thus the corresponding MI signals make promising device fingerprints, which are difficult to be modified or mimicked. We design a stimulation and a discrepancy extraction scheme and evaluate them with 90 mobile devices, including 70 laptops (among which 30 are of totally identical CPU and operating system) and 20 smartphones. The results show that
DeMiCPU
can achieve 99.7% precision and recall on average, and 99.8% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6~s. The performance can be further improved to 99.9% with multi-round fingerprinting. In addition, we implement a prototype of
DeMiCPU
docker, which can effectively reduce the requirement of test points and enlarge the fingerprinting area.
Relating confidence to measured information uncertainty in qualitative reasoning
