🤖 AI Summary
This study addresses the critical gap between theory and practice in AI-driven cyberattack prediction, focusing on outdated datasets, limited attack coverage, insufficient model interpretability, weak adversarial robustness, and privacy-ethical risks. Through a systematic review of over 150 benchmark datasets and more than 200 studies, the work introduces a novel multidimensional gap assessment framework based on detection impact, implementation cost, and remediation time to prioritize these challenges. The analysis identifies dataset obsolescence and adversarial robustness as the highest-priority issues, while highlighting interpretability as a cost-effective entry point in resource-constrained settings. Furthermore, the study proposes a tripartite classification of dataset quality—production-ready, research-only, and unusable—alongside a corresponding deployment roadmap, significantly enhancing the practical feasibility and robustness of AI-based cybersecurity systems.
📝 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.