Anomaly detection

Anomaly detection, also known as outlier detection, is a type of data analysis used to identify unexpected items or events in datasets. Anomalies can be useful in identifying potential fraud, security risks, or other information of interest.

Anomaly detection can be used to search for events or objects that don’t conform to expected behavior or are not in line with typical responses. This allows companies to identify potential risks that require attention.

Anomaly detection can be applied to many types of data, from financial and customer-related data to sensor data or log files. Different approaches are used in different contexts. For example, in credit card fraud detection, anomaly detection is used to identify uncommon card usage patterns and transactions that cannot be explained by typical user behavior.

Anomaly detection is an important tool for IT professionals, especially those in the cybersecurity field. Knowing how and when to use anomaly detection can help defend against cyber threats.

Anomaly detection is sometimes used in combination with predictive analytics. This combination is used to determine the likelihood of a particular event happening in the future based on existing data and then applying anomaly detection to detect threats or suspicious activities that exceed the model’s prediction.

The success of anomaly detection depends on the quality of inputs and data manipulation techniques. Knowing when to collect data and the type of data to collect is essential for achieving meaningful results. Proper data cleaning and preprocessing are necessary to develop models that can accurately detect anomalies.