Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X
The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more suscep...
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2023-07-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/2737 |
| _version_ | 1797771776844890112 |
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| author | Irfan Ali Kandhro Fayyaz Ali Ali Orangzeb Panhwar Raja Sohail Ahmed Larik Kanwal Fatima |
| author_facet | Irfan Ali Kandhro Fayyaz Ali Ali Orangzeb Panhwar Raja Sohail Ahmed Larik Kanwal Fatima |
| author_sort | Irfan Ali Kandhro |
| collection | DOAJ |
| description | The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more susceptible to security threats. In this paper, Autonomous vehicles (AVs) transform the intelligent transportation system by exchanging real-time and seamless data with other AVs and the network (ITS). Transportation that is automated has many advantages for people. However, worries about safety, security, and privacy continue to grow. The AVs need to exchange sensory data with other AVs and with their own for navigation and trajectory planning. When an unreliable sensor-equipped AV or one that is malicious enters connectivity in such circumstances, the results could be disruptive. To effectively detect anomalies and mitigate cyberattacks in AVs, this study suggests the Efficient Anomaly Detection (EAD) method. The EAD technique finds and isolates rogue AVs using the Multi-Agent Reinforcement Learning (MARL) algorithm, which operates over the 6G network to thwart modern cyberattacks and provide a quick and accurate anomaly detection mechanism. The expected outcomes demonstrate the value of EAD and have an accuracy rate that is 8.01% greater than that of the current systems. |
| first_indexed | 2024-03-12T21:41:36Z |
| format | Article |
| id | doaj.art-9ee4007e4429493882ca9741ba51ea4b |
| institution | Directory Open Access Journal |
| issn | 0254-7821 2413-7219 |
| language | English |
| last_indexed | 2024-03-12T21:41:36Z |
| publishDate | 2023-07-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj.art-9ee4007e4429493882ca9741ba51ea4b2023-07-26T17:25:47ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192023-07-01423798810.22581/muet1982.2303.092737Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2XIrfan Ali Kandhro0Fayyaz Ali1Ali Orangzeb Panhwar2Raja Sohail Ahmed Larik3Kanwal Fatima4Department of Computer Science, Sindh Madressatul Islam University, Karachi Sindh PakistanDepartment of Software Engineering, Sir Syed University of Engineering and Technology, Karachi Sindh PakistanDepartment of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Gharo Sindh PakistanSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, P.R. ChinaDepartment of Computer Science, Sindh Madressatul Islam University, Karachi Sindh PakistanThe development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more susceptible to security threats. In this paper, Autonomous vehicles (AVs) transform the intelligent transportation system by exchanging real-time and seamless data with other AVs and the network (ITS). Transportation that is automated has many advantages for people. However, worries about safety, security, and privacy continue to grow. The AVs need to exchange sensory data with other AVs and with their own for navigation and trajectory planning. When an unreliable sensor-equipped AV or one that is malicious enters connectivity in such circumstances, the results could be disruptive. To effectively detect anomalies and mitigate cyberattacks in AVs, this study suggests the Efficient Anomaly Detection (EAD) method. The EAD technique finds and isolates rogue AVs using the Multi-Agent Reinforcement Learning (MARL) algorithm, which operates over the 6G network to thwart modern cyberattacks and provide a quick and accurate anomaly detection mechanism. The expected outcomes demonstrate the value of EAD and have an accuracy rate that is 8.01% greater than that of the current systems.https://publications.muet.edu.pk/index.php/muetrj/article/view/2737 |
| spellingShingle | Irfan Ali Kandhro Fayyaz Ali Ali Orangzeb Panhwar Raja Sohail Ahmed Larik Kanwal Fatima Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X Mehran University Research Journal of Engineering and Technology |
| title | Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X |
| title_full | Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X |
| title_fullStr | Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X |
| title_full_unstemmed | Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X |
| title_short | Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X |
| title_sort | artificial intelligence ai empowered anomaly detection for autonomous vehicles in 6g v2x |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/2737 |
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