A Machine Learning Approach to Characterizing and Detecting Electronic Devices
Abstract
The unauthorized use of computers and electronic devices by foreign intelligence and disgruntled insiders poses a threat to the government. However, the consumption of energy by electronic devices induces measurable signatures back into the local power system. The detection and monitoring of these signatures provides a method of identifying unauthorized equipment use and potential counterintelligence problems. Previous works in this field focus on distinguishing between devices with a wide diversity in loads. We advance this research by focusing on nearly identical devices in the same class. By using device startup transient features and machine learning algorithms, we show that it is possible to identify unique devices in an electrically noisy office setting. This advances the state of the art and enables a more robust detection algorithm that is well-suited for counterintelligence efforts.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 22, 2018
- Accession Number
- AD1120362
Entities
People
- Cassie Seubert
- David Daigle