Cutting through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection

Authors: Sayakkara, Asanka; Miralles, Luis; Le-Khac, Nhien-An and Scanlon, Mark

Publication Date: March 2020

Publication Name: Forensic Science International: Digital Investigation

Abstract:

Electromagnetic side-channel analysis (EM-SCA) has been used as a window to eavesdrop on computing devices for information security purposes. It has recently been proposed to use as a digital evidence acquisition method in forensic investigation scenarios as well. The massive amount of data produced by EM signal acquisition devices makes it difficult to process them in real-time making on-sight EM-SCA nearly impossible. The uncertainty of exact information leaking frequency channel demands the investigator to acquire signals over a wide bandwidth. As a consequence, the investigators are left with a large number of potential frequency channels in EM data to be inspected, among them , many may not contain any information leakages at all. Under these circumstances, the identification of a small subset of frequency channels that leak sufficient amount of information can significantly boost the performance of real-time analysis of EM side-channel data. This work, presents a systematic methodology to identify information leaking frequency channels from high dimensional EM data with the help of multiple filtering techniques and machine learning. The evaluations show that it is possible to narrow down the number of frequency channels from over 20,000 to less than few hundreds that makes real-time EM data processing highly efficient.

BibTeX Entry:

@article{sayakkara2020EMFeatureSelection,
author={Sayakkara, Asanka and Miralles, Luis and Le-Khac, Nhien-An and Scanlon, Mark},
title="{Cutting through the Emissions: Feature Selection from Electromagnetic Side-Channel Data for Activity Detection}",
journal="{Forensic Science International: Digital Investigation}",
year="2020",
month="03",
publisher={Elsevier},
abstract={Electromagnetic side-channel analysis (EM-SCA) has been used as a window to eavesdrop on computing devices for information security purposes. It has recently been proposed to use as a digital evidence acquisition method in forensic investigation scenarios as well. The massive amount of data produced by EM signal acquisition devices makes it difficult to process them in real-time making on-sight EM-SCA nearly impossible. The uncertainty of exact information leaking frequency channel demands the investigator to acquire signals over a wide bandwidth. As a consequence, the investigators are left with a large number of potential frequency channels in EM data to be inspected, among them , many may not contain any information leakages at all. Under these circumstances, the identification of a small subset of frequency channels that leak sufficient amount of information can significantly boost the performance of real-time analysis of EM side-channel data. This work, presents a systematic methodology to identify information leaking frequency channels from high dimensional EM data with the help of multiple filtering techniques and machine learning. The evaluations show that it is possible to narrow down the number of frequency channels from over 20,000 to less than few hundreds that makes real-time EM data processing highly efficient. }
}