New orientations in the field of vehicular cybersecurity

A database from 140 scientific articles (journal and conference papers) from the automotive security domain. In the database, we assigned specific attributes to every article (such as Web of Science Impact Factor or the number of citations). The data set was analyzed by the K-means clustering and decision tree analysis methods to identify and characterize the generated groups of papers. We did not aim to identify perfectly supplementing categories but to define the relevant research topics of the automotive security domain. Following this, some of the chosen categories may have overlap with other topics, which means that these research categories may be partly laid on common scientific and professional basics. However, all the considered categories can be defined as separate, scientifically significant, and considerably relevant research orientations.本数据集源自汽车安全领域的140篇学术文献（含期刊论文与会议论文）。本数据库为每篇文献均标注了特定属性，例如Web of Science影响因子（Web of Science Impact Factor）、被引频次等。本数据集采用K-means聚类（K-means clustering）与决策树分析（decision tree analysis）方法开展分析，以识别并刻画所生成的文献群组特征。 本研究并非旨在构建完全互补的分类体系，而是为了明确汽车安全领域的相关研究主题。据此，部分选定的分类可能与其他研究主题存在重叠，即这些研究分类部分依托于共通的科学与专业基础。但所有纳入考量的分类均可被界定为独立、具备科学价值且高度相关的研究方向。