security cyber resilience drift privacy
Research gap analysis derived from 2 biology papers in our local library.
The gap
Addressing the challenges of adversarial attacks, data quality, interpretability, concept drift, scalability, and privacy requires ongoing research and development efforts to improve the robustness, resilience, and effectiveness of ML-based cyber security systems.; The framework's performance is not compared against other AI-driven cyber resilience or backup data security approaches in the literature.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- The Role of Machine Learning in Cyber Security (2026) · doi
Addressing the challenges of adversarial attacks, data quality, interpretability, concept drift, scalability, and privacy requires ongoing research and development efforts to improve the robustness, resilience, and effectiveness of ML-based cyber security systems.
Keywords: addressing challenges adversarial attacks quality interpretability concept drift scalability privacy requires ongoing development efforts improve - Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure (2026) · doi
The framework's performance is not compared against other AI-driven cyber resilience or backup data security approaches in the literature.
Keywords: framework performance compared against driven cyber resilience backup security approaches literature
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