Internet of vehicles intrusion detection method based on hierarchical feature enhancement and dynamic sparse attention mechanism

The vehicle network intrusion detection method, which utilizes hierarchical feature enhancement and dynamic sparse attention mechanism, solves the problems of insufficient detection accuracy and high computational complexity in existing technologies. It achieves efficient identification and real-time detection of various attacks and is suitable for in-vehicle environments.

CN121547769APending Publication Date: 2026-02-17HANGZHOU DIANZI UNIV
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Patent Information

Application Number
CN202511715373.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-21
Publication Date
2026-02-17

AI Technical Summary

Technical Problem

Existing intrusion detection methods for vehicle networks have shortcomings in detection accuracy, computational complexity, and insufficient utilization of temporal features. They are difficult to effectively identify multiple attack types and are difficult to deploy in real time in resource-constrained vehicle environments.

Method used

A method based on hierarchical feature enhancement and dynamic sparse attention mechanism is adopted. Through multimodal data preprocessing, lightweight convolutional autoencoder and multi-scale temporal statistical feature extraction, combined with dynamic sparse attention mechanism and multi-granularity adaptive classification, efficient detection of CAN bus messages is achieved.

Benefits of technology

It significantly improves the detection accuracy and real-time performance of various network attacks, reduces computational complexity, has adaptive defense capabilities against unknown attacks, and is suitable for resource-constrained vehicle environments.

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Abstract

The invention discloses an Internet of Vehicles intrusion detection method based on hierarchical feature enhancement and a dynamic sparse attention mechanism, and the method comprises the steps: firstly obtaining an original CAN bus message data stream, and carrying out the multi-modal data preprocessing and hierarchical feature construction; secondly, for hierarchical features, performing message-level depth feature compression and space-time statistical feature enhancement by adopting a dual-channel feature compression mechanism to form an enhanced mixed feature vector, and then constructing a neural network model of a dynamic sparse attention mechanism based on content awareness to perform feature extraction; and inputting a sparse attention calculation result into a hierarchical classifier, and predicting and outputting a detection result. And training the network model, and deploying the trained network model in a vehicle-mounted embedded device to realize real-time message monitoring and intrusion detection. According to the method, the calculation complexity and parameter quantity of the model are greatly reduced, the real-time performance in a vehicle-mounted environment is remarkably improved, and the detection capability on complex hidden attacks is effectively improved.
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