No prior work systematically evaluates whether learned

The gap

Evidence profile

Stated in the future work and synthesized and cells research gap sections of the source papers, classified as general, drawn from work published between 2023 and 2026, spanning 3 journals. Those papers have been cited 23 times in total.

Supporting evidence — 3 representative gaps

• An Explainable Ensemble Machine Learning Framework for Phishing Website Detection with Robustness and Deployment Readiness Evaluation (2026) · International Journal of Creative and Open Research in Engineering and Management · doi

  In order to further increase the reli-ability of the proposed method in the context of future cyber security scenarios, potential research topics include: •Adversarial Training: The implementation of advanced security measures manipulation feature manipulation and adversaries targeting the MLbased detection.

  generalstated in future workevidence 5/5
  Keywords: security manipulation order further increase reli ability proposed context future cyber scenarios potential topics include
• A Review of Techniques and Policies on Cybersecurity Using Artificial Intelligence and Reinforcement Learning Algorithms (2023) · IEEE Technology and Society Magazine · cited 23× · doi

  No prior work systematically evaluates whether learned attacker models (from RL or IL methods) generalize to attack scenarios beyond their training distribution, or how to measure fidelity of learned attack sequences against real Cyber Threat Intelligence traces in a unified framework.

  generalstated in synthesizedevidence 5/5
  Keywords: prior work systematically evaluates whether learned attacker models
• CYBER THREAT FORECASTING: THE TRANSITION FROM TRADITIONAL ML TO GENERATIVE AI APPROACHES (2026) · American Journal of AI Cyber Computing Management · doi

  Traditional ML models have limited adaptability to evolving attack behaviors. - Conventional cybersecurity solutions are often unable to detect emerging attack patterns. - There is a need for a comprehensive framework that integrates traditional machine learning techniques with advanced Generative AI models for cyber threat forecasting.

  generalstated in cells research gapevidence 5/5
  Keywords: traditional models have limited adaptability evolving attack behaviors

Questions about this gap