Transfer Learning-Based Anomaly Detection System for Autonomous Vehicle
The advancements in technology have brought about significant changes in the automobile industry. A system that combines the control of a physical process with computing technology and communication networks is called a cyber–physical system (CPS). The enhancement of network communication has transi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/58/1/90 |
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| Summary: | The advancements in technology have brought about significant changes in the automobile industry. A system that combines the control of a physical process with computing technology and communication networks is called a cyber–physical system (CPS). The enhancement of network communication has transitioned vehicles from purely mechanical to software-controlled technologies. The controller area network (CAN) bus protocol controls the communication network of autonomous vehicles. The convergence of technologies in autonomous vehicles (AVs) and connected vehicles (CVs) within Connected and Autonomous Vehicles (CAVs) leads to improved traffic flow, enhanced safety, and increased reliability. CAVs development and deployment have gained momentum, and many companies and research organizations have announced their initiatives and begun road trials. Governments worldwide have also implemented policies to facilitate and expedite the deployment of CAVs. Nevertheless, the issue of CAV cyber security has become a prevalent concern, representing a significant challenge in deploying CAVs. This study presents an intelligent cyber threat detection system (ICTDS) for CAV that utilizes transfer learning to detect cyberattacks on physical components of autonomous vehicles through their network infrastructure. The proposed security system was tested using an autonomous vehicle network dataset. The dataset was preprocessed and used to train and evaluate various pre-trained convolutional neural networks (CNNs), such as ResNet-50, MobileNetV2, AlexNet, GoogLeNet and YOLOV8. The proposed security system demonstrated exceptional performance, as demonstrated by its results in precision, recall, F1-score, and accuracy metrics. The system achieved an accuracy rate of 99.90%, indicating its high level of performance. |
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| ISSN: | 2673-4591 |