Physical-Layer Cyberattack and Interference Resilient Automotive Radars
In this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack si...
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doaj-da0bacb68e6d4ec5bf49237aa039bf792021-03-30T04:02:52ZengIEEEIEEE Access2169-35362020-01-01821553121554310.1109/ACCESS.2020.30416219274416Physical-Layer Cyberattack and Interference Resilient Automotive RadarsOnur Toker0https://orcid.org/0000-0003-2580-5449Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL, USAIn this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack signal level is above a defined relative threshold, both the probability of false attack alarm and the probability of not detecting an attack converge to zero exponentially with the number of samples acquired during a single chirp, and the number of chirps used in a frame. We also derive an analytical formula for this relative threshold, and prove that by selecting shorter frame durations, and using lower noise RF equipment, the threshold can be made as small as possible. Basically, by proper selection of radar parameters arbitrarily small attack signals can be detected almost always with almost no false alarms. We also present a numerical example using real measured data obtained from two 77 GHz automotive radars operated at the same time. Also using real data, we show that the proposed system reduces the negative effects of undetected weak attacks which are below the above mentioned threshold.https://ieeexplore.ieee.org/document/9274416/Automotive radarsattack resiliencecybersecurityinterference resiliencephysical-layer attacks |
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language |
English |
format |
Article |
sources |
DOAJ |
author |
Onur Toker |
spellingShingle |
Onur Toker Physical-Layer Cyberattack and Interference Resilient Automotive Radars IEEE Access Automotive radars attack resilience cybersecurity interference resilience physical-layer attacks |
author_facet |
Onur Toker |
author_sort |
Onur Toker |
title |
Physical-Layer Cyberattack and Interference Resilient Automotive Radars |
title_short |
Physical-Layer Cyberattack and Interference Resilient Automotive Radars |
title_full |
Physical-Layer Cyberattack and Interference Resilient Automotive Radars |
title_fullStr |
Physical-Layer Cyberattack and Interference Resilient Automotive Radars |
title_full_unstemmed |
Physical-Layer Cyberattack and Interference Resilient Automotive Radars |
title_sort |
physical-layer cyberattack and interference resilient automotive radars |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
In this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack signal level is above a defined relative threshold, both the probability of false attack alarm and the probability of not detecting an attack converge to zero exponentially with the number of samples acquired during a single chirp, and the number of chirps used in a frame. We also derive an analytical formula for this relative threshold, and prove that by selecting shorter frame durations, and using lower noise RF equipment, the threshold can be made as small as possible. Basically, by proper selection of radar parameters arbitrarily small attack signals can be detected almost always with almost no false alarms. We also present a numerical example using real measured data obtained from two 77 GHz automotive radars operated at the same time. Also using real data, we show that the proposed system reduces the negative effects of undetected weak attacks which are below the above mentioned threshold. |
topic |
Automotive radars attack resilience cybersecurity interference resilience physical-layer attacks |
url |
https://ieeexplore.ieee.org/document/9274416/ |
work_keys_str_mv |
AT onurtoker physicallayercyberattackandinterferenceresilientautomotiveradars |
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1724182460459122688 |