Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach
With the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisi...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10819009/ |
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| author | Tianyi Li Mingfeng Shang Shian Wang Raphael Stern |
| author_facet | Tianyi Li Mingfeng Shang Shian Wang Raphael Stern |
| author_sort | Tianyi Li |
| collection | DOAJ |
| description | With the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisions are more conspicuous, subtle attacks that slightly modify driving behaviors can cause widespread impacts, including increased congestion, fuel consumption, and crash risks without being easily detected. To address the detection of such attacks, we first present a traffic modeling framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, data poison attacks, and denial-of-service (DoS) attacks. Subsequently, we examine the consequences of these attacks on both singular vehicle dynamics (micro) and broader traffic flow patterns (macro). We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. Numerical results are presented to show the effectiveness of our machine learning strategy in identifying cyberattacks on vehicles equipped with ACC. The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles. |
| format | Article |
| id | doaj-art-d86f2ffdfff44dd9931cd13b674d33e8 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-d86f2ffdfff44dd9931cd13b674d33e82025-01-22T00:00:24ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-016112310.1109/OJITS.2024.352296910819009Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning ApproachTianyi Li0https://orcid.org/0000-0002-6075-6411Mingfeng Shang1https://orcid.org/0000-0003-1192-8472Shian Wang2https://orcid.org/0000-0003-1644-3570Raphael Stern3https://orcid.org/0000-0001-6633-7827Department of Civil Engineering, Saint Louis University, Saint Louis, MO, USADepartment of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ, USADepartment of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, USADepartment of Civil, Environmental, and Geo- Engineering, University of Minnesota, Minneapolis, MN, USAWith the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisions are more conspicuous, subtle attacks that slightly modify driving behaviors can cause widespread impacts, including increased congestion, fuel consumption, and crash risks without being easily detected. To address the detection of such attacks, we first present a traffic modeling framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, data poison attacks, and denial-of-service (DoS) attacks. Subsequently, we examine the consequences of these attacks on both singular vehicle dynamics (micro) and broader traffic flow patterns (macro). We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. Numerical results are presented to show the effectiveness of our machine learning strategy in identifying cyberattacks on vehicles equipped with ACC. The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.https://ieeexplore.ieee.org/document/10819009/Adaptive cruise control (ACC) vehicleautomated vehicletransportation cybersecuritymachine learning |
| spellingShingle | Tianyi Li Mingfeng Shang Shian Wang Raphael Stern Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach IEEE Open Journal of Intelligent Transportation Systems Adaptive cruise control (ACC) vehicle automated vehicle transportation cybersecurity machine learning |
| title | Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach |
| title_full | Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach |
| title_fullStr | Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach |
| title_full_unstemmed | Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach |
| title_short | Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach |
| title_sort | detecting subtle cyberattacks on adaptive cruise control vehicles a machine learning approach |
| topic | Adaptive cruise control (ACC) vehicle automated vehicle transportation cybersecurity machine learning |
| url | https://ieeexplore.ieee.org/document/10819009/ |
| work_keys_str_mv | AT tianyili detectingsubtlecyberattacksonadaptivecruisecontrolvehiclesamachinelearningapproach AT mingfengshang detectingsubtlecyberattacksonadaptivecruisecontrolvehiclesamachinelearningapproach AT shianwang detectingsubtlecyberattacksonadaptivecruisecontrolvehiclesamachinelearningapproach AT raphaelstern detectingsubtlecyberattacksonadaptivecruisecontrolvehiclesamachinelearningapproach |