Foundational Cybersecurity Sciences

To prepare for a future of advanced cyber threats and diversifying operational systems, NLR works to advance the core science of cybersecurity and transmit breakthroughs into field-ready solutions.

Capabilities

Artificial Intelligence

Our research is focused on building strong defenses that take full advantage of advancements in artificial intelligence (AI) for the security of future energy systems, while also countering AI-assisted attacks in an expanding attack surface.

Current research areas include:

• Reinforcement learning for automated cyber response and recovery
• Adversarial learning for enhancing cyber resilience
• Federated learning approaches for privacy-preserving cyber resilience
• Explainable AI to enable the adoption and deployment of AI-based cybersecurity tools.

Formal Methods for Cybersecurity of Future Energy Systems

Formal methods refer to a field of study that combines formal logic, computer science, and mathematics to create automated frameworks to guarantee system properties (e.g., cyber resilience) and verify system behaviors (e.g., cyber controls). These can be expressed in a formal language and systematically and rigorously proven using computer assistance. NLR is working on formal methods for developing tools and approaches for verifiable operational technology cybersecurity, which features increasing complexity and interconnectedness, including:

• Cybersecurity for high-assurance applications
• Formal verification for cybersecurity requirements and tools
• Formal requirements analysis for cybersecurity.

Cryptographic Approaches for Cybersecurity of Future Energy Systems

NLR is developing fundamental cryptographic approaches that support zero-trust security in operational systems. The NLR team has explored novel techniques such as zero-knowledge proofs to develop cyber-resilient grid controls and communications that are robust in a post-quantum world. In addition, the team has developed a validated and system-ready tool, Module-OT, that sits between system assets as a bump-in-the-wire security appliance, cryptographically protecting both modern and legacy devices. Other ongoing work in this area includes:

• Zero-knowledge proofs for operational technology cybersecurity
• Quantum-resistant algorithms.

Cyber Resilience by Design

Engineered systems should incorporate security and resilience from initial concept and design as primary features rather than afterthoughts. NLR promotes this concept through various approaches, including cyber-informed engineering, which updates engineering certifications and education with cybersecurity principles, including:

• A cyber resilience-by-design framework for the assessment of cyber-physical systems
• An AI framework to enable cyber resilience leveraging cyber-physical network topologies.

NLR is coleading the Cyber-Informed Engineering program with Idaho National Laboratory to advance cyber-informed engineering in the interest of designing secure energy systems.

Selected Publications

Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning, AI (2025)

Designing Future Energy Systems With Generative AI, Computing in Science & Engineering (2025)

Demystifying Cyberattacks: Potential for Securing Energy Systems With Explainable AI, International Conference on Computing, Networking and Communications (2024)

Verifying the Computational Integrity of Power Grid Controls With Zero-Knowledge Proof, IEEE Power and Energy Society General Meeting (2023)

A Randomization-Based, Zero-Trust Cyberattack Detection Method for Hierarchical Systems, IEEE Secure Development Conference (2023)

Reinforcement Learning Environment for Cyber-Resilient Power Distribution System, IEEE Access (2023)

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems, IEEE Access (2022)

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