Recently, vehicles have experienced a rise in networking and informatization, leading to increased security concerns. As the most widely used automotive bus network, the Controller Area Network (CAN) bus is vulnerable to attacks, as security was not considered in its original design. This paper proposes SIDuBzip2, a traffic anomaly detection method for the CAN bus based on the bzip2 compression algorithm. The proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic, constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal traffic. The method consists of three parts: the conversion of CAN ID values to characters, the calculation of similarity based on bzip2 compression, and the optimal solution of model parameters. The experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks, including Denial of Service , replay, basic injection, mixed injection, and suppression attacks. In addition, existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay, demonstrating the feasibility of the proposed method.

We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.