ARMOR AI for Water Treatment
Anomaly detection for safer, more secure SCADA systems
Unlock the Future of Operational Efficiency with ARMOR Anomaly Detection
The water treatment sector is a critical infrastructure that underpins national security, economic stability, and technological progress. SCADA systems, in wide use in the industry, play a pivotal role in ensuring operational efficiency and reliability. Yet, their critical importance also makes them a prime target for increasingly sophisticated and evolving cyber threats. This threat is exacerbated by the vast volumes of telemetry, system logs, and alarms generated by SCADA systems, which often obscure patterns of malicious activity and increase the likelihood of delayed or missed anomaly detection.
The catastrophic consequences of an undetected or poorly mitigated threat can compromise operations, impose severe economic damage, and endanger public safety. The proliferation of digital equipment and Internet of Things (IoT) technologies has amplified the complexity of these environments, further broadening vulnerabilities and intensifying the demand for advanced anomaly detection methodologies.
Building on these challenges it is imperative to deploy robust solutions that adapt to the dynamic and interconnected nature of SCADA systems.
Lymba introduces ARMOR-AI, a novel unsupervised learning AI-driven cybersecurity system designed for real-time detection and diagnosis of malicious activities in SCADA environments.
Unlike conventional security approaches that rely on signature-based detection, our system integrates deep learning, Generative Adversarial Networks (GANs), and multimodal fusion into a unified, adaptive defense mechanism. This architecture not only identifies known attack patterns but also detects previously unseen cyber threats by learning complex dependencies across diverse SCADA data streams.
This holistic approach overcomes single-modal limitations by capturing interdependencies among diverse data types while remaining adaptable to new sources.
Real-world attack scenario data is scarce, making supervised learning approaches impractical. GANs circumvent this limitation by training exclusively on normal operational data, allowing them to adapt dynamically to evolving threats. Additionally, they enhance the robustness of anomaly detection systems by generating diverse synthetic scenarios that improve the model’s ability to detect previously unseen attack patterns.
Contact us today to learn more about how Lymba can improve your facility’s safety and security