ARMOR    Anomaly Recognition through Multi-modal Observation & Reasoning

Detect Anomalies in Real Time


Use the power of AI to revolutionize the way you protect systems and data, and harness the power of anomaly detection.

Overview

ARMOR is a versatile, multi-modal unsupervised learning framework engineered to detect and interpret anomalous events within complex environments. By seamlessly integrating data from diverse modalities such as textual, auditory, and visual, ARMOR delivers a comprehensive, context-aware understanding of a system’s behavior. At its foundation, ARMOR captures and models the interdependencies between modalities, ensuring that insights from one data stream are analyzed in the context of complementary signals from others. This multi-perspective reasoning is essential in environments where anomalies manifest heterogeneously across different data channels.

Adaptability

AI-powered anomaly detection can analyze vast amounts of data from SCADA systems, network traffic, user behaviors, and system logs to identify subtle deviations that might signify novel attacks, including zero-day threats.

Unlike traditional security measures, AI-based anomaly detection can adapt to new and unknown threats, making it particularly effective against evolving cyber risks.

Unlike conventional approaches that depend on predefined classes or supervised labels, ARMOR is trained exclusively on patterns of normal behavior, enabling it to identify and flag previously unseen anomalies across a broad spectrum of applications.


Detailed Explanations

Upon detecting an anomaly, ARMOR activates its integrated explanation module, providing clear and actionable insights that include: a detailed justification for why the anomaly was flagged, casual reasoning based on the interplay and relationships across multiple modalities, and targeted recommendations for corrective or preventative measures. This explanation capability is firmly grounded in domain-specific principles and can be further enhanced with ground truth knowledge and other actionable intelligence critical for high-stakes environments.

Types of Detected Anomalies

From electrical grids to water distribution networks, ARMOR identifies a wide range of anomalies across industrial domains. It excels at spotting both point anomalies, such as sudden spikes or drops, and collective anomalies, such as gradual degradation or systemic shifts. Leveraging insights from benchmark datasets like SKAB, SMAP, and WADI, ARMOR is trained to model complex cross-sensor dependencies and adapt to real-world variability. The result is a system that outperforms traditional approaches by delivering high-precision, context-aware anomaly detection across critical infrastructure.