A method and system for detecting intrusion of CAN bus based on deep belief network
By employing a CAN bus intrusion detection method based on deep belief networks, and utilizing CAN message rule filters and deep belief network model training, the problems of low accuracy and high latency in CAN bus intrusion detection are solved, achieving a more efficient detection effect.
CN116545700BActive Publication Date: 2026-05-29WUHAN UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN UNIV
- Filing Date
- 2023-05-08
- Publication Date
- 2026-05-29
AI Technical Summary
Technical Problem
Existing CAN bus intrusion detection methods suffer from low detection accuracy and long detection delay.
Method used
A CAN bus intrusion detection method based on deep belief networks is adopted. By acquiring a pre-set training dataset and a test dataset, a CAN message rule filter and an initial detection model are constructed. The deep belief network is used for model training, and the CAN message filter is used for preprocessing and detection.
Benefits of technology
It improves detection accuracy, reduces detection time overhead, enhances the detection accuracy against hybrid attacks, reduces false alarm rate, and achieves higher detection precision.
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Smart Images
Figure CN116545700B_ABST
Abstract
The application provides a CAN bus intrusion detection method and system based on a deep belief network, and belongs to the technical field of intrusion detection, and comprises the following steps: determining a controller area network (CAN) message rule filter and an initial detection model based on a deep belief network; using different preset training data sets and preset test data sets to train the CAN message rule filter and the initial detection model to obtain an intrusion combination detection model; preprocessing a CAN message to be detected to obtain a pretreated message; inputting the pretreated message into the intrusion combination detection model to output a CAN bus intrusion detection result. In a vehicle-mounted environment, the filter is constructed by using CAN messages, the detection precision is improved, the time cost in the detection process is reduced, the problem that an attack data set is difficult to obtain is overcome, all normal messages without attacks are used for model training, the detection accuracy for mixed attacks is improved, and better false positive rates and precision can be achieved.
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