Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption

Area of Science:

• Electrical Engineering
• Computer Science
• Cybersecurity

Background:

• Device authentication is crucial for security.
• Existing methods rely on software or hardware characteristics.
• A passive, externally measurable fingerprinting technique is needed.

Purpose of the Study:

• To propose a novel device fingerprinting technique using electrical current consumption.
• To assess the feasibility of identifying same-model devices based on minute current variations.
• To evaluate the performance of various time-series classification methods for this task.

Main Methods:

• Collected current consumption signals from 40 same-model computer displays.
• Utilized state-of-the-art time-series classification (TSC) methods.
• Compared performance of different TSC techniques, including Empirical Wavelet Transform (EWT) and Linear Discriminant Analysis (LDA).

Main Results:

• Successfully identified 40 similar electrical devices with approximately 94% precision.
• Most classification errors occurred between a small subset of devices.
• A simplified Empirical Wavelet Transform (EWT) combined with Linear Discriminant Analysis (LDA) was identified as the optimal classification method.

Conclusions:

• Electrical current consumption can serve as a reliable device fingerprint for authentication.
• The proposed EWT-LDA method demonstrates high accuracy in distinguishing between similar devices.
• This passive, externally measured approach offers a practical solution for device identification and security.