ARMOR AI for Food & Beverage
Anomaly detection for improved efficiency, safety, and consistency in SCADA systems
Unlock the Future of Operational Efficiency with ARMOR Anomaly Detection
Food and Beverage Producers earn customers with quality and safe production. They keep customers with consistency. SCADA systems, in wide use in the industry, play a pivotal role in ensuring these operational standards every day. Yet, their critical importance also comes with increasing complexity. This is exacerbated by the vast volumes of telemetry, system logs, and alarms generated by SCADA systems, which often obscure patterns of unusual activity and increase the likelihood of delayed or missed anomaly detection which could lead to product recalls.
The catastrophic consequences of an undetected or poorly mitigated threat can compromise operations, impose severe economic damage, and ever 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, an AI-driven cybersecurity system for real-time detection and diagnosis of malicious activities in SCADA environments. ARMOR-AI is designed to uncover irregularities quicker to keep production lines moving and mitigate potential recalls.
Unlike conventional systems monitoring that rely on signature-based detection, our system integrates deep learning, Generative Adversarial Networks (GANs), and multimodal fusion into a unified, adaptive mechanism. This architecture not only identifies known anomaly patterns but also detects previously unseen inconsistencies 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 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 problems.
Contact us today to learn more about how Lymba can improve your facility’s operations