Artificial intelligence is reshaping cybersecurity, but much of that progress has focused on cloud and enterprise ...

In today’s increasingly complex software environments, the automatic detection and analysis of anomalies within system logs has become indispensable. Log anomaly detection encompasses a range of ...

Intrusion Detection Systems (IDS) and anomaly detection techniques underpin modern cybersecurity by autonomously monitoring network activities and flagging deviations from normal behaviour. IDS are ...

Anomaly detection is the process of identifying events or patterns that differ from expected behavior. Anomaly detection can range from simple outlier detection to complex machine learning algorithms ...

Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...

Discover how to secure AI orchestration workflows using post-quantum cryptography and AI-driven anomaly detection for Model Context Protocol (MCP) environments.

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

In today’s digital world, fraud has become more complex, which means we need smarter ways to detect and prevent it. Generative AI helps with this by looking at large amounts of data in real-time, ...

Black Book outlines an AI-era integrity architecture for healthcare benchmarking, instrumentation hardening, tiered ...

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 ...