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Conference proceeding
Vehicle Controller Area Network Inspection Using Recurrent Neural Networks
Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), pp.494-499
Lecture Notes in Networks and Systems
International First International Conference on Advances in Computing Research (ACR 2023) (Orlando, Florida, USA, 05/08/2023–05/10/2023)
2023
Abstract
The increasing connectivity in vehicles brings the potential for cyber-attacks, which can result in safety hazards or vehicle malfunctioning. Therefore, it is crucial to develop novel methods that can protect the vehicular network from malicious actors. In this paper, we propose a method of utilizing PCAP (Packet Capture) payloads extracted from the Controller Area Network (CAN) of a sample vehicle dataset, and applying a Recurrent Neural Network (RNN) to detect malicious or benign activity in the dataset. The process converts the extracted hexadecimal values of the payload to tensors and are labeled as either malicious or benign. The tensors are then passed through a neural network to produce the outputs. Our method showed to detect 78% of all packets within the dataset, which indicated its effectiveness in identifying cyberattacks in vehicular networks. This approach can eventually be applied to real-world scenarios, where the detection and prevention of malicious activity can make vehicles more secure.
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Details
- Title
- Vehicle Controller Area Network Inspection Using Recurrent Neural Networks
- Publication Details
- Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), pp.494-499
- Resource Type
- Conference proceeding
- Conference
- International First International Conference on Advances in Computing Research (ACR 2023) (Orlando, Florida, USA, 05/08/2023–05/10/2023)
- Publisher
- Springer Nature Switzerland; Cham
- Series
- Lecture Notes in Networks and Systems
- Number of pages
- 6
- Copyright
- © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
- Identifiers
- 99381591125606600
- Academic Unit
- Center for Cybersecurity; Division of Academic Affairs
- Language
- English