Research Paper

Core Innovation

This paper presents i-SDT, combining hydraulically-regularized predictive modeling with multi-class attack discrimination and adaptive control. It uses Temporal Convolutional Networks with conservation constraints and a recurrent residual encoder with Maximum Mean Discrepancy to separate normal and attack states, enabling safe operation without shutdowns.

Why It Matters

Industrial control systems face increasing cyber threats that can cause costly shutdowns and operational disruptions. This solution improves attack detection accuracy and discriminates attack types, enabling continuous safe operation and reducing false alarms and costs. It scales to critical infrastructure, enhancing security and operational resilience in real time.

Potential Customers & Pain Points

• Critical infrastructure operators – Need to prevent costly shutdowns from cyber-attacks
• Industrial control system providers – Need accurate real-time attack detection and classification
• Cybersecurity firms – Need advanced tools for resilient control and anomaly discrimination.

Business Model

Subscription-based SaaS platform with tiered pricing for detection, classification, and control modules; enterprise licensing for critical infrastructure operators.

Competitive Landscape

• Nozomi Networks
• Claroty
• Dragos
• Darktrace

Implementation Challenges

• Integration complexity with diverse industrial control systems
• Adoption resistance due to operational risk concerns
• Need for extensive validation in varied real-world environments

Validation Strategy

• Pilot deployments with industrial control system operators
• Benchmarking against existing cybersecurity detection tools
• Simulation-in-the-loop testing on diverse industrial datasets