Visible to the public Identification of Computer Displays Through Their Electromagnetic Emissions Using Support Vector Machines

TitleIdentification of Computer Displays Through Their Electromagnetic Emissions Using Support Vector Machines
Publication TypeConference Paper
Year of Publication2020
AuthorsEfendioglu, H. S., Asik, U., Karadeniz, C.
Conference Name2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
KeywordsAntenna measurements, artificial intelligence, classification, composability, computer displays, Computer graphics, confusion matrix, display image capture, electromagnetic emissions, Electromagnetics, F1-score, image classification, Image reconstruction, Information security, Kernel, matrix algebra, Monitoring, pattern classification, Predictive Metrics, pubcrawl, Resiliency, security of data, Signal processing, Support vector machines, tempest, TEMPEST information security problem, Training
AbstractAs a TEMPEST information security problem, electromagnetic emissions from the computer displays can be captured, and reconstructed using signal processing techniques. It is necessary to identify the display type to intercept the image of the display. To determine the display type not only significant for attackers but also for protectors to prevent display compromising emanations. This study relates to the identification of the display type using Support Vector Machines (SVM) from electromagnetic emissions emitted from computer displays. After measuring the emissions using receiver measurement system, the signals were processed and training/test data sets were formed and the classification performance of the displays was examined with the SVM. Moreover, solutions for a better classification under real conditions have been proposed. Thus, one of the important step of the display image capture can accomplished by automatically identification the display types. The performance of the proposed method was evaluated in terms of confusion matrix and accuracy, precision, F1-score, recall performance measures.
Citation Keyefendioglu_identification_2020