Anomaly Detection refers to the process of identifying unusual patterns or data points in a dataset that deviate from the norm. It is widely used in fraud detection, network security, and monitoring systems.
How is Anomaly Detection used in AI?
Anomaly Detection in AI is used to identify abnormal behavior in datasets, such as detecting fraud, monitoring network traffic, and predicting equipment failures.
What techniques are used for Anomaly Detection?
Common techniques for Anomaly Detection include statistical methods, machine learning algorithms, and deep learning models, each tailored to specific types of data and use cases.
What is the difference between Anomaly Detection and Negative Testing?
Anomaly Detection identifies unexpected patterns in data, while Negative Testing is a software testing method that checks how the system behaves under invalid or unexpected inputs.