发表机构
Karabuk University（卡拉比克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究定量分析了2019至2026年间37项基于图的网络攻击检测研究，发现自动表示学习策略占主导（73.0%），且应用领域与表示策略存在显著关联。
AI 中文摘要
基于图的网络攻击检测研究在不同网络安全应用领域中采用了各种图构建和表示策略。这种多样性促使我们对表示策略在这些应用领域中的分布情况进行定量考察。本研究对2019年至2026年间发表的37项原始研究进行了定量分析。每项研究均根据发表年份、应用领域、图表示类型、特征提取策略、学习范式、算法和数据集进行编码。采用了频率分析、交叉制表和统计关联检验。自动表示学习是最常用的策略，占研究的73.0%，而手工表示策略占27.0%。Fisher Freeman Halton精确检验揭示了应用领域与表示策略之间存在统计学上显著的关联（精确p = 0.008；Cramer's V = 0.540）。研究结果表明，在所分析的文献中，自动表示学习占主导地位，且表示策略的分布在不同网络安全应用领域中存在差异。
英文摘要
Graph based cyber attack detection studies employ various graph construction and representation strategies across different cybersecurity application domains. This diversity motivates a quantitative examination of how representation strategies are distributed across these application domains. This study presents a quantitative analysis of 37 original studies published between 2019 and 2026. Each study was coded according to publication year, application domain, graph representation type, feature extraction strategy, learning paradigm, algorithm, and dataset. Frequency analysis, cross tabulation, and statistical association tests were applied. Automated representation learning was the most frequently employed strategy, accounting for 73.0% of the studies, while handcrafted representation strategies accounted for 27.0%. The Fisher Freeman Halton exact test revealed a statistically significant association between application domain and representation strategy (exact p = 0.008; Cramer's V = 0.540). The findings indicate that automated representation learning predominates within the analyzed corpus and that the distribution of representation strategies differs across cybersecurity application domains.
