Ensuring Cross-Device Portability of Electromagnetic Side-Channel Analysis for Digital Forensics

Navanesan, Lojenaa; Le-Khac, Nhien-An; Scanlon, Mark; De Zoysa, Kasun; Sayakkara, Asanka P.

Publication Date:  March 2024

Publication Name:  Forensic Science International: Digital Investigation

Abstract:   Investigation on smart devices has become an essential subdomain in digital forensics. The inherent diversity and complexity of smart devices pose a challenge to the extraction of evidence without physically tampering with it, which is often a strict requirement in law enforcement and legal proceedings. Recently, this has led to the application of non-intrusive Electromagnetic Side-Channel Analysis (EM-SCA) as an emerging approach to extract forensic insights from smart devices. EM-SCA for digital forensics is still in its infancy, and has only been tested on a small number of devices so far. Most importantly, the question still remains whether Machine Learning (ML) models in EM-SCA are portable across multiple devices to be useful in digital forensics, i.e., cross-device portability. This study experimentally explores this aspect of EM-SCA using a wide set of smart devices. The experiments using various iPhones and Nordic Semiconductor nRF52-DK devices indicate that the direct application of pre-trained ML models across multiple identical devices does not yield optimal outcomes (under 20% accuracy in most cases). Subsequent experiments included collecting distinct samples of EM traces from all the devices to train new ML models with mixed device data; this also fell short of expectations (still below 20% accuracy). This prompted the adoption of transfer learning techniques, which showed promise for cross-model implementations. In particular, for the iPhone 13 and nRF52-DK devices, applying transfer learning techniques resulted in achieving the highest accuracy, with accuracy scores of 98% and 96%, respectively. This result makes a significant advancement in the application of EM-SCA to digital forensics by enabling the use of pre-trained models across identical or similar devices.

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      @article{navanesan2024crossdeviceEMSCA,
author="Navanesan, Lojenaa and Le-Khac, Nhien-An and Scanlon, Mark and De Zoysa, Kasun and Sayakkara, Asanka P.",
title="Ensuring Cross-Device Portability of Electromagnetic Side-Channel Analysis for Digital Forensics",
booktitle="Forensic Science International: Digital Investigation",
year=2024,
month=03,
publisher="Elsevier",
abstract="Investigation on smart devices has become an essential subdomain in digital forensics. The inherent diversity and complexity of smart devices pose a challenge to the extraction of evidence without physically tampering with it, which is often a strict requirement in law enforcement and legal proceedings. Recently, this has led to the application of non-intrusive Electromagnetic Side-Channel Analysis (EM-SCA) as an emerging approach to extract forensic insights from smart devices. EM-SCA for digital forensics is still in its infancy, and has only been tested on a small number of devices so far. Most importantly, the question still remains whether Machine Learning (ML) models in EM-SCA are portable across multiple devices to be useful in digital forensics, i.e., cross-device portability. This study experimentally explores this aspect of EM-SCA using a wide set of smart devices. The experiments using various iPhones and Nordic Semiconductor nRF52-DK devices indicate that the direct application of pre-trained ML models across multiple identical devices does not yield optimal outcomes (under 20% accuracy in most cases). Subsequent experiments included collecting distinct samples of EM traces from all the devices to train new ML models with mixed device data; this also fell short of expectations (still below 20% accuracy). This prompted the adoption of transfer learning techniques, which showed promise for cross-model implementations. In particular, for the iPhone 13 and nRF52-DK devices, applying transfer learning techniques resulted in achieving the highest accuracy, with accuracy scores of 98% and 96%, respectively. This result makes a significant advancement in the application of EM-SCA to digital forensics by enabling the use of pre-trained models across identical or similar devices."
}