Researchers from Politecnico di Milano propose a data-driven water leak detection method that treats leaks as anomalies in ...

Magnetic anomaly detection (MAD) techniques have long been employed to identify subtle deviations in the Earth’s ambient magnetic field, which often indicate the presence of concealed ferromagnetic ...

Explore how AI-driven anomaly detection enhances the security of Model Context Protocol (MCP) deployments, protecting AI infrastructure from evolving threats with real-time insights.

Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...

Explore strategies for managing combinatorial explosion in high dimensional anomaly detection to enhance data observability ...

Organizations today rely heavily on data to inform their decision-making processes at every level. However, the increasing complexity of data ecosystems poses a challenge: The data we rely on may not ...

The NDR market is expanding due to increased cloud, remote work, and IoT adoption, creating complex attack surfaces. Opportunities include AI-driven anomaly detection, integration with EDR, XDR, SIEM, ...

Unlike pattern-matching, which is about spotting connections and relationships, when we detect anomalies we are seeing disconnections—things that do not fit together. Anomalies get much less attention ...