Threat Detection & CPS Resilience

Data-driven detection and resilience for cyber-physical and networked systems.

We build data-driven methods to detect threats and keep cyber-physical and networked systems resilient under attack — from forecasting vulnerability trends to evaluating access-control policies for data security.

Related publications

  • Autonomous system vulnerability remediation: A survey of agentic AI, reinforcement learning, benchmarks, and operational safety
    A Orojo, W Elumelu, E El-Mahmoud, E Leal · (2026)
  • Assessing the Impact of Access Control Policies on Data Accessibility in Distributed NoSQL Environments
    A Orojo, E El-Mahmoud, G Speegle · The Twenty Fourth International Conference on Security & Management 25, 2025 (2025)
  • A Unified Framework Incorporating AW-TRBAC and Semantic Variational Autoencoders for Dynamic Threat Detection and Access Control
    A Orojo, E El-Mahmoud, S Hutton, M Donahoo, WC Elumelu · International Conference on Artificial Intelligence 2025, 2025 (2025)
  • Predicting software vulnerability trends with multi-recurrent neural networks: a time series forecasting approach
    AK Orojo, WC Elumelu, OO Orojo · Proceedings of the First International Conference on Natural Language …, 2024 (2024)