Anomaly detection

Anomaly Detection is a type of computer security technique used to detect and identify suspicious data or activities that are potentially linked to security threats or malicious attacks. It is a form of unsupervised machine learning that relies on existing data to detect unknown and unexpected irregularities in the data that are potentially suspicious.

Anomaly detection methods are used to detect abnormalities in fixed-length input features, such as network application traffic, computer system data or log entries, text messages, or other data sources. It is also used to detect unusual behavior from users or devices, intra and inter network traffic, and other activities. The models for anomaly detection typically use supervised or unsupervised learning methods to detect outliers, vary in the amount of information used from the data, and must be trained on a set of labeled data.

Anomaly detection works by scanning for data points that diverge from the expected behavior or patterns in a dataset. This could include unusual activity on customer accounts, suspicious network traffic, potential fraud, or any other type of data point that stands out from the rest of the data. When an unusual pattern is detected, the system can be used to alert an operator or initiate an automated response to the threat.

Anomaly detection systems can be used for cyber security, intelligence gathering, fraud detection, manufacturing defect detection and healthcare data analysis. This type of detection can also be used in healthcare to detect medical problems, to diagnose equipment malfunctions or failure, or to detect financial fraud.

Anomaly detection is a powerful tool for detecting threats or malicious behavior. However, due to the complexity of some datasets, false positives or false negatives may occur with these models—where an abnormal event is wrongly detected or a malicious behavior goes undetected. To reduce these false positives and negatives, anomaly detection models must be carefully tuned and periodically re-evaluated.

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