Anomaly detection, also called outlier detection, is the process of finding rare items in a dataset. Examples include finding fraudulent login events and fake news items. Take a look at the demo ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...

In cybersecurity, anomaly detection in tabular data is essential for ensuring information security. While traditional machine learning and deep learning methods have shown some success, they continue ...

Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset ...

Anomaly detection can be powerful in spotting cyber incidents, but experts say CISOs should balance traditional signature-based detection with more bespoke methods that can identify malicious activity ...