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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2013-04-27
A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation
By
Fan Mo,

    Dr. Fan Mo

    School of Electronic Engineering

    Beijing University of Posts and Telecommunications

    China

    Homepage

Ying-Hua Lu,

    Dr. Ying-Hua Lu

    School of Electronic Engineering

    Beijing University of Posts and Telecommunications

    China

    Homepage

Jinling Zhang,

    Dr. Jinling Zhang

    School of Electronic Engineering

    Beijing University of Posts and Telecommunications

    China

    Homepage

Qiang Cui,

    Dr. Qiang Cui

    China Electronics Standardization Institute

    China

    Homepage

Sihai Qiu

    Dr. Sihai Qiu

    Department of Electrical Engineering

    Beijing University of Posts and Telecommunications

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 30, 211-224, 2013
doi:10.2528/PIERM12122406 | Google Scholar

Abstract
Electrical equipments usually radiate unintended emission which carries characteristic information when running, such as emanation from computers monitors, keyboards and other components, this emanation can be possibly used to reconstruct the source information. Most of the experiments related to this area are carried out inside a semi-anechoic chamber, and measurement out of it may not be considered to be optimal, because the data captured are usually not sufficient. Yet in this study, we take LCD monitors as typical examples and find that characteristics significantly differ between products, parameters such as the magnitude and spectrum were measured under normal environment. We take the PCB traces as antennas and acquire the raw signal directly near the antenna and extract the parameters to use as input to support vector machine (SVM) which was trained to identify the emanating source(LCD monitors). In this study, the method was tested using the emission captured from one Samsung (SyncMaster E1920) and two LG (L1753s) monitors, and a laptop(ACER Aspire 5542). The SVM was able to classify the source of signals with 98.9510% accuracy while using emission that captured from the running monitors.
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Citation
Fan Mo, Ying-Hua Lu, Jinling Zhang, Qiang Cui, and Sihai Qiu, "A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation," Progress In Electromagnetics Research M, Vol. 30, 211-224, 2013.
doi:10.2528/PIERM12122406
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