Operationalizing Cyber Attack Prediction: A Gap-Prioritized Framework with Dataset and Model Selection Guidelines

• URL: http://arxiv.org/abs/2606.03386v1
• Date: Tue, 02 Jun 2026 09:29:53 GMT
• Title: Operationalizing Cyber Attack Prediction: A Gap-Prioritized Framework with Dataset and Model Selection Guidelines
• Abstract summary: This paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identify and prioritize five implementation hurdles.<n>We introduce a novel gap-prioritization framework that evaluates these limitations based on detection impact, implementation cost, and remediation time.<n>This work translates academic findings into actionable decision-support tools for robust, production-oriented AI-driven cyber defense.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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