Jayita Gulati provides an overview:
Anomaly detection means finding patterns in data that are different from normal. These unusual patterns are called anomalies or outliers. In large datasets, finding anomalies is harder. The data is big, and patterns can be complex. Regular methods may not work well because there is so much data to look through. Special techniques are needed to find these rare patterns quickly and easily. These methods help in many areas, like banking, healthcare, and security.
Let’s have a concise look at anomaly detection techniques for use on large scale datasets. This will be no-frills, and be straight to the point in order for you to follow up with additional materials where you see fit.
Outlier detection is a large an interesting space. I suppose I should shill for myself a little bit and note that I wrote a book on the topic. This post provides some quick guidance around outlier detection techniques and applications, and serves as a fine starting point for digging in further.