Device Fingerprinting with Magnetic Induction Signals Radiated by CPU Modules

Abstract

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.

Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption

“…A very low-frequency sampling rate of 50 kHz was applied. This sampling frequency is below the commonly applied bandwidth in RF-based fingerprinting [ 14 ] and is four times smaller than the one reported in the magnetic induction fingerprinting study reported in [ 2 ]. It is also lower than the switching frequency of common switched-mode power supplies (SMPSs) (see also Section 2 below).…”

“…We have chosen relatively simple devices (computer monitors) with a single mode of operation. This is in contrast to computers [ 2 ] or cyber-physical systems (CPS) [ 9 , 10 ], where differences between complex modes of operation may be identified.…”

“…The proposed measurement is completely passive, with all the devices having a similar unsupervised mode of operation. This is in contrast to the situation with dedicated supervision, such as a computer system that runs on particular calculations [ 2 , 6 , 7 ].…”

“…For example, remote identification of operating systems by the analysis of clock skews of the same communication protocol was provided in [1]. Ji et al [2] showed effective fingerprinting of laptops with the same software and hardware by utilizing slight differences in the CPU's emission of magnetic induction. It was shown that differences in the power consumption profiles of a mobile phone might be used to identify the currently used application [3].…”

“…The proposed measurement is completely passive, with all the devices having a similar unsupervised mode of operation. This is in contrast to the situation with dedicated supervision, such as a computer system that runs on particular calculations [2,6,7]. • Devices of interest do not include multiple feature-rich protocols, such as in the field of fingerprinting communications devices [11][12][13].…”