网络攻击预测的操作化：一个带有数据集和模型选择指南的差距优先级框架
Operationalizing Cyber Attack Prediction: A Gap-Prioritized Framework with Dataset and Model Selection Guidelines
AI总结：
本文提出一个差距优先级框架，通过分析150+基准数据集和200+研究，识别并优先处理五个实施障碍，并提供数据集质量评估框架和实用路线图，以弥合网络攻击预测研究与实践之间的差距。
摘要：
尽管用于网络攻击预测的AI和机器学习已经取得进展，但理论研究与实际操作部署之间仍存在关键差距。基于Ankalaki等人（2025）的工作，本文对150多个基准数据集和200多项研究进行了全面分析，以识别并优先处理五个实施障碍：（1）时间数据集过时，（2）攻击范围狭窄，（3）实时模型可解释性，（4）对抗鲁棒性不足，以及（5）隐私/伦理问题。我们引入了一个新颖的差距优先级框架，根据检测影响、实施成本和修复时间评估这些限制。我们的分析将数据集过时和对抗鲁棒性确定为最高优先级的差距，同时强调模型可解释性是资源受限环境中成本效益最高的路径。为了弥合研究与实践之间的鸿沟，我们提供了一个实用的实施路线图和一个数据集质量评估框架，将45个基准分类为生产就绪、仅限研究和不可用三类。这项工作将学术发现转化为可操作的决策支持工具，用于健壮的、面向生产的AI驱动网络防御。
英文摘要：
While AI and machine learning for cyber attack prediction have advanced, a critical gap persists between theoretical research and practical operational deployment. Building on Ankalaki et al. (2025), this paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identify and prioritize five implementation hurdles: (1) temporal dataset obsolescence, (2) narrow attack scope, (3) real-time model interpretability, (4) inadequate adversarial robustness, and (5) privacy/ethical concerns. We introduce a novel gap-prioritization framework that evaluates these limitations based on detection impact, implementation cost, and remediation time. Our analysis identifies dataset obsolescence and adversarial robustness as the highest-priority gaps, while highlighting model interpretability as the most cost-effective path for resource-constrained environments. To bridge the research-practice divide, we provide a practical implementation roadmap and a dataset quality assessment framework that classifies 45 benchmarks into production-ready, research-only, and unusable categories. This work translates academic findings into actionable decision-support tools for robust, production-oriented AI-driven cyber defense.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
