Anomaly detection in the Controller Area Network (CAN) bus is critical for ensuring the security and reliability of intelligent connected vehicles, which are increasingly prevalent. While existing anomaly detection strategies offer some benefits, they often face challenges such as limited feature extraction and data imbalance, which reduce their effectiveness. To address these issues, in this paper, we propose an unsupervised intrusion detection method based on CAN message graph named CGTS. Specifically, we first construct a message graph based on CAN message sequences. A Graph Transformer is then employed to extract complex structural information, accurately capturing the intrinsic connections between messages. Furthermore, to address the data imbalance problem, we integrate the Support Vector Data Description algorithm after the Graph Transformer model. This algorithm identifies anomalous behaviors efficiently without relying on a priori labels. Experiments conducted on public datasets, including Car-Hacking and CAN-Train-and-Test, demonstrate the efficacy of CGTS. The model achieves an average accuracy exceeding 0.990, precision above 0.995, and an F1-score nearing 0.993. These results highlight CGTS can effectively detect multiple injection attacks and significantly improve the CAN bus intrusion detection performance.

Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining prior knowledge of noise distributions. Moreover, few algorithms are specifically designed to leverage the unique spatial structural information inherent in 3D seismic data, leading to inefficient utilization of this valuable information during denoising. To address these issues, we propose Self-Supervised Seismic Denoising using the Denoising Diffusion Probabilistic Model (SSDn-DDPM), an algorithm specifically tailored for 3D seismic data that utilizes diffusion generative models for self-supervised blind denoising. The algorithm begins with self-supervised modeling of seismic noise to estimate its distribution. Subsequently, spatial structural information of 3D seismic data is leveraged to improve the accuracy of noise distribution estimation. Furthermore, the algorithm integrates the noise distribution estimation network into the diffusion model to further guide and refine the sampling process, thereby optimizing computational complexity and improving detail representation. Finally, it performs self-supervised 3D seismic noise suppression using the diffusion probabilistic model. In the experimental section, we comprehensively compare the proposed algorithm with six different types of seismic denoising methods. Various comparative experiments demonstrate that the proposed algorithm achieves exceptional denoising performance on 3D seismic data, even without ground truth or any prior knowledge about the noise distribution.