A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks
This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green l...
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doaj-4358838c3dfa4145af2ab9ea98276cb22020-11-25T04:00:29ZengMDPI AGInformation2078-24892020-10-011149649610.3390/info11110496A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular NetworksBartłomiej Płaczek0Marcin Bernas1Marcin Cholewa2Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, PolandDepartment of Computer Science and Automatics, University of Bielsko-Biała, ul. Willowa 2, 43-309 Bielsko-Biała, PolandInstitute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, PolandThis paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control.https://www.mdpi.com/2078-2489/11/11/496vehicular networkvehicular ad-hoc network (VANET)malicious datatraffic signalssmart cityintelligent transportation systems |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bartłomiej Płaczek Marcin Bernas Marcin Cholewa |
spellingShingle |
Bartłomiej Płaczek Marcin Bernas Marcin Cholewa A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks Information vehicular network vehicular ad-hoc network (VANET) malicious data traffic signals smart city intelligent transportation systems |
author_facet |
Bartłomiej Płaczek Marcin Bernas Marcin Cholewa |
author_sort |
Bartłomiej Płaczek |
title |
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks |
title_short |
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks |
title_full |
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks |
title_fullStr |
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks |
title_full_unstemmed |
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks |
title_sort |
credibility score algorithm for malicious data detection in urban vehicular networks |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2020-10-01 |
description |
This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control. |
topic |
vehicular network vehicular ad-hoc network (VANET) malicious data traffic signals smart city intelligent transportation systems |
url |
https://www.mdpi.com/2078-2489/11/11/496 |
work_keys_str_mv |
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