AI-Driven Cybersecurity for Nuclear Infrastructure

Securing Critical Nuclear Infrastructure

Modern nuclear facilities face escalating cybersecurity challenges as operational technology (OT) and information technology (IT) systems become increasingly interconnected. Unlike traditional IT security threats, cyber attacks targeting nuclear infrastructure can manipulate physical processes through sensor spoofing, control system interference, and data integrity compromises that evade conventional network-based detection methods. The complexity of these threats demands novel approaches that leverage both artificial intelligence capabilities and deep understanding of nuclear system physics.

Historical incidents in critical infrastructure have demonstrated the vulnerability of industrial control systems to sophisticated adversaries capable of leveraging cyber-physical attack vectors. The 2010 Stuxnet attack on Iranian nuclear facilities exemplified how malware could target programmable logic controllers to cause physical damage while remaining undetected for extended periods. Subsequent attacks on power grids, water treatment facilities, and manufacturing systems have highlighted the urgent need for robust, AI-driven detection capabilities specifically designed for cyber-physical environments.

Robust Experimental Framework

Our research addresses this critical gap by developing and systematically evaluating AI-driven cybersecurity approaches using the Mechanisms Engineering Test Loop (METL) at Argonne National Laboratory as a realistic experimental platform. METL features integrated test vessels operating at temperatures up to 1,200 degrees F, electromagnetic pumps, and comprehensive instrumentation and control (I&C) systems including Emerson/NI CRIO devices and industrial controllers. This architecture provides authentic thermal-hydraulic conditions and sensor networks for developing AI-driven anomaly detection and cyber-physical security models.

Data Architecture and Processing Pipeline

The experimental framework establishes a comprehensive cybersecurity testbed that spans the complete data lifecycle from operational sensor collection through machine learning evaluation. This testbed architecture enables systematic analysis of attack detection capabilities under realistic conditions while maintaining complete experimental control and reproducibility.

Machine Learning Evaluation Framework

Our evaluation framework implements four complementary machine learning detection paradigms, each designed to capture different aspects of anomalous behavior in cyber-physical systems:

• Change Point Detection: Utilizes streaming statistical baseline learning to identify abrupt transitions in sensor data patterns, employing adaptive statistical monitoring with 95th percentile detection thresholds.
• LSTM-based Anomaly Detection: Employs a 4-layer tapering architecture with 40→32 → 24 → 16 hidden units and 25% dropout regularization, processing 50-timestep sequences with per-sensor specialization.
• Dependency Violation analysis: Implements a three-analyzer ensemble approach (correlation, Granger causality, Random Forest) monitoring the 100 most statistically significant sensor pairs.
• Autoencoder reconstruction methods: Utilize a dense architecture with 4-layer encoder and symmetric decoder, creating a 4-dimensional latent bottleneck, using maximum reconstruction error for anomaly detection.

Performance Benchmarks & Attack Detectability

Our experimental evaluation provided insights into AI-based cybersecurity detection capabilities for nuclear infrastructure. The evaluation of four machine learning paradigms demonstrated clear performance hierarchies and attack-specific vulnerabilities that inform operational deployment strategies.

Machine Learning Paradigm Performance

Attack Detectability Analysis

Attack detectability varied dramatically across the 15 scenarios, revealing critical insights for defense strategy development:

• Multi-site coordinated attacks proved most detectable (AUC = 0.739), due to broad impact across multiple sensor networks.
• Physics violation scenarios achieved exceptional detectability (AUC = 0.903), being obvious attacks violating fundamental thermodynamic relationships.
• Precision trust decay attacks presented the greatest detection challenge (AUC = 0.592), demonstrating sophisticated evasion through gradual sensor degradation and noise injection.
• Replay-based false data injection attacks achieved moderate evasion success (AUC = 0.643), with effectiveness varying by replay duration and timing synchronization.

Strategic Deployment & Future Directions

The experimental results demonstrate that paradigm-attack matching is critical for operational deployment, with no single detection method providing comprehensive coverage across the diverse threat landscape. The 35% performance differential between Change Point Detection (0.785) and Autoencoder methods (0.580) highlights the importance of selecting appropriate algorithms for specific threat profiles and operational requirements.

Deployment Recommendations:

• Implement Change Point Detection for primary monitoring of abrupt operational changes.
• Deploy LSTM-based methods for consistent baseline performance across diverse threat scenarios.
• Utilize Dependency Violation analysis for physics-based attack detection in correlation-sensitive systems.
• Employ Autoencoder methods as specialized detectors for subtle distribution shifts and sophisticated evasion techniques.

These findings suggest that ensemble approaches combining complementary detection paradigms could enhance overall coverage while addressing individual paradigm limitations. The integration of physics-based detection methods could provide additional robustness against sophisticated evasion techniques.

Future Research Directions:

Focus on ensemble method development, combining the complementary strengths of different detection paradigms to achieve comprehensive threat coverage. Integration of physics-based detection methods with data-driven approaches represents a particularly promising avenue, leveraging immutable physical laws as foundational elements in cybersecurity architectures. The establishment of quantitative performance benchmarks through this work provides essential foundations for operational deployment decisions and regulatory framework development in nuclear cybersecurity.