Designing an Adaptive AI-Enhanced Cybersecurity Framework for Real-Time Threat Mitigation in Critical Infrastructure

@article{1710731,
      author = {Sarat Kehinde Akinade},
      title = {Designing an Adaptive AI-Enhanced Cybersecurity Framework for Real-Time Threat Mitigation in Critical Infrastructure},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {1146-1150},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710731.pdf},
      abstract = {The critical infrastructure industries (energy, transport, water, healthcare, telecommunications) are under constant attack from high impact cyber threats that require real-time automated detection and context-sensitive response. This paper outlines the design of an adaptive, AI-integrated automated cybersecurity framework for real-time threat mitigation of critical infrastructure. It combines continuous adaptive monitoring, automated anomaly detection, decision orchestration, and human-in-process control oversight. This framework makes an equilibrium trade-off of detection accuracy, explanation, resilience to adversarial counteraction, and regulation compliance. It is informed by recent work in adaptive anomaly detection, the NIST AI risk management and cybersecurity guidance, and recent research in AI-for-cybersecurity. This paper proposes a questionnaire-based evaluation framework, illustrates possible readiness gaps with simulated data (n = 120), and provides actionable strategies for incremental implementation and operational testing.},
      keywords = {AI, Cybersecurity, Infrastructure, Real Time, Ai-Driven},
      month = {August},
      doi = {https://doi.org/10.64388/IREV8I2-1710731-3691}
  }