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.

The proposed industrial anomaly detection model is computationally efficient, memory-friendly, and also suitable for low-light conditions, common in manufacturing environments, making it well-suited ...

In industry, the detection of anomalies such as scratches, dents, and discolorations is crucial to ensure product quality and safety. However, conventional methods rely on heavy computational ...

The most important challenge to the security of blockchains remains the protection of users from malicious signature requests as the adoption of cryptocurrencies goes on to gain even more momentum. A ...

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 ...

An AI model trained to detect abnormalities on breast MR images accurately depicted tumor locations and outperformed benchmark models when tested in three different groups, according to a study ...