Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connecte...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3035741 |
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| _version_ | 1832561140523073536 |
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| author | Seunghyun Park Jin-Young Choi |
| author_facet | Seunghyun Park Jin-Young Choi |
| author_sort | Seunghyun Park |
| collection | DOAJ |
| description | The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms. |
| format | Article |
| id | doaj-art-467f94b2a1424fb59c31938d99984c23 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-467f94b2a1424fb59c31938d99984c232025-02-03T01:25:50ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/30357413035741Malware Detection in Self-Driving Vehicles Using Machine Learning AlgorithmsSeunghyun Park0Jin-Young Choi1Graduate School of Information Security, Korea University, Seoul 02841, Republic of KoreaGraduate School of Information Security, Korea University, Seoul 02841, Republic of KoreaThe recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.http://dx.doi.org/10.1155/2020/3035741 |
| spellingShingle | Seunghyun Park Jin-Young Choi Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms Journal of Advanced Transportation |
| title | Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms |
| title_full | Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms |
| title_fullStr | Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms |
| title_full_unstemmed | Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms |
| title_short | Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms |
| title_sort | malware detection in self driving vehicles using machine learning algorithms |
| url | http://dx.doi.org/10.1155/2020/3035741 |
| work_keys_str_mv | AT seunghyunpark malwaredetectioninselfdrivingvehiclesusingmachinelearningalgorithms AT jinyoungchoi malwaredetectioninselfdrivingvehiclesusingmachinelearningalgorithms |