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CAN-Bus Attack Detection With Deep Learning
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DOI：10.1109/TITS.2020.3046974.png)
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Abstract
En 中文
Modern cars include a huge number of sensors and actuators, which continuously exchange data and control commands. The most used protocol for communication of different components in automotive system is the Controller Area Network (CAN). According to CAN, components communicate by broadcasting messages on a bus. In addition, the standard definition of the protocol does not provide information for authentication, so exposing it to attacks. This paper proposes a method based on deep learning aiming at discovering attacks towards the CAN-bus. In particular, Neural Networks and MultiLayer Perceptrons are the class of networks employed in our approach. We also validate our approach by analysing a real-world dataset with the injection of messages from different types of attacks: denial of service, fuzzy pattern attacks, and attacks against specific components. The obtained results are encouraging and demonstrate the effectiveness of the approach.
Keywords:
Standards
Deep learning
Automobiles
Protocols
Electronic mail
Biological neural networks
Wireless communication
Automotive
intelligent systems
deep learning
neural networks
attack detection
security
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