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Anomaly detection identifies data that doesn’t fit a set or schema, indicating a system problem or intrusion. It’s valuable for security and fraud detection in various industries, including finance and science. Automated systems can generate reports and alerts for further action.
Anomaly detection is an automated process that identifies data that does not belong to one set or schema. Mismatched data can be a sign of a problem with a system, and in large data streams, users may not be able to detect the anomaly. The automated system can identify it, collect information and generate a report. Some systems may even be equipped to intervene if an anomaly is an identifiable problem and needs some kind of system response to protect the system or users.
Faults can occur for a variety of reasons. One is a failure with a system that causes garbled, incomplete, or corrupted data to be generated. A system can also have abnormal data due to an intrusion, where the data can either be an injection from another source or a virus that is proliferating within the system. Fraud can also generate anomalies in a computer system.
From a systems architecture and security perspective, anomaly detection is a valuable tool. Automated scanning can identify and block many attacks before the user even notices them, and this can make the whole system much more secure. Whether errors are the result of an internal problem or an external attack, they must be identified and resolved as quickly as possible. If the system detects an anomaly and doesn’t know how to respond, it can send a report to a system administrator for further action.
Fraud detection can also be important. Insurance companies and other organizations can run anomaly detection scans on claims and reports to see if any stand out or appear unusual. This can help them identify obvious cases of fraud. Similarly, banks and other financial firms use anomaly detection for security. If a 90-year-old person with a very stable banking history suddenly starts behaving strangely, for example, the anomaly detection system could flag it and indicate suspected identity theft.
Anomaly detection is also a useful tool in the sciences. Researchers can use this tool to locate rogue microorganisms, DNA, and other bits of data of interest in a sample. This can help them identify the source of a medical problem, track down and eliminate impurities in a sample, and perform other tasks. In epidemiology, for example, automated programs scan health care facility reports for outliers that could be warning signs of an emerging epidemic, and can send alerts to researchers and public health officials if anything unusual is detected.
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