Detection and Suppression of Intentional EMI Attacks to Smart Speakers

A new digital technique to detect and suppress intentional electromagnetic interference (IEMI) attacks on smart speakers (SSs) without disrupting their nominal operation during the attack is proposed in this article. The effectiveness of the proposed approach is verified by experiments on a proof-of-concept SS prototype equipped with an artificial intelligence speech recognition algorithm. Thanks to the proposed suppression technique, the power of malicious signal in the audio band is suppressed by 19 dB, thus effectively preventing the execution of malicious commands in more than 99.6% of the cases under IEMI attacks of up to 5 dBm injected RF power in 10–100 MHz bandwidth. Under the same test conditions, the proposed detection technique, operating independently from the suppression method, makes it possible to successfully identify IEMI attacks in more than 99.8% of the cases. Experimental results also show the proper recognition of nominal commands while IEMI attacks are performed.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Electromagnetic Compatibility
Published
2024-11-18
DOI
https://doi.org/10.1109/temc.2024.3490953
Primary Topic
Electrostatic Discharge in Electronics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection and Suppression of Intentional EMI Attacks to Smart Speakers

Ahmed Abdullah, Paolo Crovetti, Francesco Musolino
IEEE Transactions on Electromagnetic Compatibility
Electrostatic Discharge in Electronics
article

Detection and Suppression of Intentional EMI Attacks to Smart Speakers

Ahmed Abdullah, Paolo Crovetti, Francesco Musolino
article en

Abstract

A new digital technique to detect and suppress intentional electromagnetic interference (IEMI) attacks on smart speakers (SSs) without disrupting their nominal operation during the attack is proposed in this article. The effectiveness of the proposed approach is verified by experiments on a proof-of-concept SS prototype equipped with an artificial intelligence speech recognition algorithm. Thanks to the proposed suppression technique, the power of malicious signal in the audio band is suppressed by 19 dB, thus effectively preventing the execution of malicious commands in more than 99.6% of the cases under IEMI attacks of up to 5 dBm injected RF power in 10–100 MHz bandwidth. Under the same test conditions, the proposed detection technique, operating independently from the suppression method, makes it possible to successfully identify IEMI attacks in more than 99.8% of the cases. Experimental results also show the proper recognition of nominal commands while IEMI attacks are performed.

IEEE Transactions on Electromagnetic CompatibilityVol. 67(2)
Politecnico di Torino (IT)
Openalex Percentile: Top 82%
Electrostatic Discharge in Electronics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.