@inproceedings{ea66f9ee8b3445678a950f46005599b5,
title = "Glowworm Attack: Optical TEMPEST Sound Recovery via a Device's Power Indicator LED",
abstract = "Two main classes of optical TEMPEST attacks against the confidentiality of information processed/delivered by devices have been demonstrated in the past two decades; the first class includes methods for recovering content from monitors, and the second class includes methods for recovering keystrokes from physical and virtual keyboards. In this paper, we identify a new class of optical TEMPEST attacks: recovering sound by analyzing optical emanations from a device's power indicator LED. We analyze the response of the power indicator LED of various devices to sound and show that there is an optical correlation between the sound that is played by connected speakers and the intensity of their power indicator LED due to the facts that: (1) the power indicator LED of various devices is connected directly to the power line, (2) the intensity of a device's power indicator LED is correlative to the power consumption, and (3) many devices lack a dedicated means of countering this phenomenon. Based on our findings, we present the Glowworm attack, an optical TEMPEST attack that can be used by eavesdroppers to recover sound by analyzing optical measurements obtained via an electro-optical sensor directed at the power indicator LED of various devices (e.g., speakers, USB hub splitters, and microcontrollers). We propose an optical-audio transformation (OAT) to recover sound in which we isolate the speech from optical measurements obtained by directing an electro-optical sensor at a device's power indicator LED. Finally, we test the performance of the Glowworm attack in various experimental setups and show that an eavesdropper can apply the attack to recover speech from speakers' power LED indicator with good intelligibility from a distance of 15 meters and with fair intelligibility from 35 meters.",
keywords = "IoT, privacy, sound recovery, tempest",
author = "Ben Nassi and Yaron Pirutin and Tomer Galor and Yuval Elovici and Boris Zadov",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 27th ACM Annual Conference on Computer and Communication Security, CCS 2021 ; Conference date: 15-11-2021 Through 19-11-2021",
year = "2021",
month = nov,
day = "12",
doi = "https://doi.org/10.1145/3460120.3484775",
language = "American English",
series = "Proceedings of the ACM Conference on Computer and Communications Security",
pages = "1900--1914",
booktitle = "CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security",
}