AI Security

Applying and stress-testing machine learning and large language models for security.

We study how machine learning and large language models can strengthen security — and how they fail. Work spans byte-level models, agentic AI systems, and representation learning for security tasks.

Related publications

  • A Unified Framework Incorporating AW-TRBAC and Semantic Variational Autoencoders for Dynamic Threat Detection and Access Control
    A Orojo, E El-Mahmoud, S Hutton, W Elumelu, M Donahoo · International Conference on Artificial Intelligence 2025, 2025 (2025)
  • ByteFlow: A Byte-Level LLM for Deep Packet Inspection and Network Intelligence
    A Orojo, E El-Mahmoud, E Leal, P Rivas · 2025 Annual Computer Security Applications Conference Workshops (ACSAC …, 2025 (2025)
  • Baylor Environmental AI Research System (BEARS): An Agentic AI Project to Combat Climate Change
    A Orojo, B Khanal, E El-Mahmoud, J Yu, MB Rashid, P Quansah, ... · 27th International Conference on Artificial Intelligence 27, 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)
  • Developing a Deep Learning Model for Detecting Cyber Attack
    AK Orojo · (2023)