A Quantitative Analysis of Graph Representation Strategies for Cyber Attack Detection

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2610.04019 [cs.AI]

(or arXiv:2610.04019v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.04019 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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