A Fuzz Testing Method Based on DDPM for Intelligent Connected Vehicles CAN Communication Beijing University of Posts and Telecommunications

Format:

Book

Conference/Event

Author/Creator:

Shen, Lin, author.

Contributor:

Xiu, Jiapeng

Yang, Zhengqiu

Zhang, Zhuopeng

Conference Name:

SAE 2024 Intelligent and Connected Vehicles Symposium (2024-09-22 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Modern vehicles are increasingly integrating electronic control units (ECUs), enhancing their intelligence but also amplifying potential security threats. Vehicle network security testing is crucial for ensuring the safety of passengers and vehicles. ECUs communicate via the in-vehicle network, adhering to the Controller Area Network (CAN) bus protocol. Due to its exposed interfaces, lack of data encryption, and absence of identity authentication, the CAN network is susceptible to exploitation by attackers. Fuzz testing is a critical technique for uncovering vulnerabilities in CAN network. However, existing fuzz testing methods primarily generate message randomly, lacking learning from the data, which results in numerous ineffective test cases, affecting the efficiency of fuzz testing. To improve the effectiveness and specificity of testing, understanding of the CAN message format is essential. However, the communication matrix of CAN messages is proprietary to the Original Equipment Manufacturer (OEM) and varie s among different models of the same vehicle brand, requiring manual reverse analysis of the CAN protocol, which significantly increases the cost and complexity of an attack. To enhance the efficiency of fuzz testing data generation, a fuzz testing data generation algorithm based on Denoising Diffusion Probabilistic Models (DDPMs) is proposed. This method learns the distribution of existing CAN bus message data, enabling the generation of data messages similar to the original data distribution for testing purposes. Furthermore, the LoRA fine-tuning method is introduced to accommodate the differences between communication matrices of various vehicles. Comparative analysis indicates that this method can generate fuzz test messages that more closely resemble real message data than existing methods

Notes:

Vendor supplied data

Publisher Number:

2024-01-7044

Access Restriction:

Restricted for use by site license