mlsanityData to 14 Oct 2026

A Survey of Electromagnetic Side-Channel Attacks and Discussion on their Case-Progressing Potential for Digital Forensics

arXiv 1903.07703 ↗First posted 18 Mar 2019cs.CRcited by 3

Not on the map. Why?

Abstract (arXiv)

The increasing prevalence of Internet of Things (IoT) devices has made it inevitable that their pertinence to digital forensic investigations will increase into the foreseeable future. These devices produced by various vendors often posses limited standard interfaces for communication, such as USB ports or WiFi/Bluetooth wireless interfaces. Meanwhile, with an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices, and IoT devices are no exception. Under these circumstances, a significant challenge is presented to digital forensic investigations where data from IoT devices needs to be analysed. This work explores the electromagnetic (EM) side-channel analysis literature for the purpose of assisting digital forensic investigations on IoT devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices. The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. The literature on various EM side-channel analysis attack techniques are discussed - selected on the basis of their applicability in IoT device investigation scenarios. The insight gained from the background study is used to identify promising future applications of the technique for digital forensic analysis on IoT devices - potentially progressing a wide variety of currently hindered digital investigations.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1.

3 papers.