An annotation and indexing model based on automotive cybersecurity test cases
Mingming Yu, Zhen Guo, Yuqiao Ning, Qianchuang Zhi, Tianling Liu, Dongqing Sun · 2024
This paper proposes an annotation and indexing model based on automotive cybersecurity test cases, which includes: firstly, annotating the test cases and establishing a label list; then, counting the frequency of occurrence and weight of high-frequency words in the label list, and constructing a retrieval model; finally, inputting the contents of the test cases entered by the user into the retrieval model, get the preliminary retrieval results, and determine the final retrieval results according to the operation of the test cases. The model proposed in this paper can effectively improve the retrieval speed of cybersecurity test cases for intelligent connected vehicles. In addition, this paper develops a whole-vehicle software test bed system to realize the digital management of automotive cybersecurity test cases.