arXiv：cs.AI· Ali Melih Kanca, Ilker Turker·· 5 小时前AI 评分30
自然可见图拓扑指标的多方法重要性与性能效率分析：面向网络攻击检测
A Multi Method Importance and Performance Efficiency Analysis of Topological Metrics for Natural Visibility Graph Based Cyber Attack Detection
AI 导读
一项研究评估了21个自然可见图（NVG）拓扑指标，用SHAP、分组Permutation Importance、Boruta和RFE四种方法结合共识排名筛选紧凑子集，在CICIDS2018数据集和CNN分类器上测试。
正文
Abstract:Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties. However, not all descriptors contribute equally to cyber-attack classification, and extracting a large metric set can increase computational cost. This study evaluates 21 NVG derived topological metrics and investigates whether a compact subset can preserve classification capability while improving computational efficiency. Four importance analysis methods SHAP, grouped Permutation Importance, Boruta, and Recursive Feature Elimination (RFE) are integrated through a Consensus Ranking strategy. Based on this ranking, Full21, Top15, Top10, Top7, Top5, and Top3 configurations are evaluated using the CICIDS2018 dataset, a CNN classifier, and stratified 5 fold cross validation. The three highest ranked metrics are avg_clustering_coeff_median, avg_clustering_coeff_std, and avg_clustering_coeff_mean. Top3 achieved the highest observed mean performance, with 97.148% accuracy, 97.055% weighted F1 score, and an MCC of 0.9675, compared with 95.999%, 95.521%, and 0.9549 for Full21, respectively. It also reduced total runtime from 14,961.39 s to 589.22 s (96.06%). These results indicate that importance guided metric reduction can provide a compact NVG representation with higher observed mean predictive performance and substantially lower computational cost under the evaluated setting.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02342 [cs.AI] |
| (or arXiv:2610.02342v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02342 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ali Melih Kanca [view email]
[v1]
Thu, 1 Oct 2026 18:15:38 UTC (544 KB)
来源：arXiv：cs.AI · arxiv.org