Fatal attraction

Perez, Beatrice; Musolesi, Mirco; Stringhini, Gianluca

doi:10.1145/3317549.3319726

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Another line of work demonstrated that electromagnetic emissions from smartphone processors could reveal information about ongoing activities, allowing device identification [41] and website/app fingerprinting [31]. However, they make impractical assumptions about device usage (e.g., no background activity and only one tab open in a browser).…”

Section: Related Workmentioning

confidence: 99%

iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches

Mohamed

Farrukh

et al. 2023

PoPETs

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We show a new type of side-channel leakage in which the built-in magnetometer sensor in Apple's mobile devices captures touch events of users. When a conductive material such as the human body touches the mobile device screen, the electric current passes through the screen capacitors generating an electromagnetic field around the touch point. This electromagnetic field leads to a sharp fluctuation in the magnetometer signals when a touch occurs, both when the mobile device is stationary and held in hand naturally. These signals can be accessed by mobile applications running in the background without requiring any permissions. We develop iSTELAN, a three-stage attack, which exploits this side-channel to infer users' application and touch data. iSTELAN translates the magnetometer signals to a binary sequence to reveal users' touch events, exploits touch event patterns to fingerprint the type of application a user is using, and models touch events to identify users' touch event types performed on different applications. We demonstrate the iSTELAN attack on 22 users while using 7 popular app types and show that it achieves an average accuracy of 90% for disclosing touch events, 74% for classifying application type used, and 73% for detecting touch event types.

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Section: Related Workmentioning

confidence: 99%

iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches

Mohamed

Farrukh

et al. 2023

PoPETs

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“…Radio frequency distinct native attributes (RF-DNA) is a method to create a unique sequence of numbers representing a specific device based on the EM radiation it produces [9], [10]. Furthermore, the magnetic field caused by smartphones are shown to be detectable with the help of built-in magnetic sensors of another smartphone in order to fingerprint such devices [11]. Such device fingerprinting techniques can be used in forensic investigation contexts to recognise a particular smartphone make and model before proceeding to extract forensic insights.…”

Section: Related Workmentioning

confidence: 99%

Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

Sayakkara

Le-Khac

2021

IEEE Access

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The increasing use of smartphones has increased their presence in legal and corporate investigations. Unlike desktop and laptop computers, forensic analysis of smartphones is a challenging task due to their limited interfaces to retrieve information of forensic value. Electromagnetic side-channel analysis (EM-SCA) has been recently proposed as an alternative window to acquire forensic insights from computers, in particularly from Internet of Things devices. Along this line, this work experimentally evaluates the potential of extracting information of forensic value from smartphones through their EM radiation. Initially, a group of smartphones representing a diverse set of system-on-chip (SoC) processors were used to acquire EM radiation traces. Later, deep learning models were trained to detect various internal software behaviours running on the SoCs. The results of this work indicates that a wide variety of insights can be extracted from smartphones through EM side-channel, increasing the potential opportunities for digital forensic investigators.INDEX TERMS Digital forensics, smartphone forensics, electromagnetic side-channel, software behaviour detection, deep learning model.

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MarcoPolo: A Zero-Permission Attack for Location Type Inference from the Magnetic Field Using Mobile Devices

Perez,

Mehrotra,

Musolesi

2024

Lecture Notes in Computer Science

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Fatal attraction

Cited by 7 publications

References 23 publications

iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches

iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches

Forensic Insights From Smartphones Through Electromagnetic Side-Channel Analysis

MarcoPolo: A Zero-Permission Attack for Location Type Inference from the Magnetic Field Using Mobile Devices

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