Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model
Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. T...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11045378/ |
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| author | Zawar Hussain Khan Faryal Ali Thomas Aaron Gulliver Ahmed B. Altamimi Mohammad Alsaffar Fahad F. Alfaisal |
| author_facet | Zawar Hussain Khan Faryal Ali Thomas Aaron Gulliver Ahmed B. Altamimi Mohammad Alsaffar Fahad F. Alfaisal |
| author_sort | Zawar Hussain Khan |
| collection | DOAJ |
| description | Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular road for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction times and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats. |
| format | Article |
| id | doaj-art-0a721c8516ff49c9b2d63d67fd11da90 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0a721c8516ff49c9b2d63d67fd11da902025-08-20T03:26:49ZengIEEEIEEE Access2169-35362025-01-011310864110865210.1109/ACCESS.2025.358178811045378Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic ModelZawar Hussain Khan0https://orcid.org/0000-0002-3007-265XFaryal Ali1https://orcid.org/0000-0001-7477-2204Thomas Aaron Gulliver2https://orcid.org/0000-0001-9919-0323Ahmed B. Altamimi3https://orcid.org/0000-0003-2893-0042Mohammad Alsaffar4https://orcid.org/0000-0001-8116-5322Fahad F. Alfaisal5College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC, CanadaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC, CanadaCollege of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaCollege of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi ArabiaDepartment of Information and Computer Science, University of Ha’il, Ha’il, Saudi ArabiaCybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular road for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction times and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats.https://ieeexplore.ieee.org/document/11045378/Connected autonomous vehicle (CAV)CAV penetration ratecyberattackmicroscopic traffic modelstability |
| spellingShingle | Zawar Hussain Khan Faryal Ali Thomas Aaron Gulliver Ahmed B. Altamimi Mohammad Alsaffar Fahad F. Alfaisal Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model IEEE Access Connected autonomous vehicle (CAV) CAV penetration rate cyberattack microscopic traffic model stability |
| title | Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model |
| title_full | Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model |
| title_fullStr | Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model |
| title_full_unstemmed | Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model |
| title_short | Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model |
| title_sort | evaluating the effects of cyberattacks in mixed and fully connected vehicle environments using a novel microscopic traffic model |
| topic | Connected autonomous vehicle (CAV) CAV penetration rate cyberattack microscopic traffic model stability |
| url | https://ieeexplore.ieee.org/document/11045378/ |
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