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Anomaly detection

The process of identifying unusual patterns or behaviors in data that do not conform to expected norms.

Description

Anomaly detection in the context of Non-Human Identities (NHIs) refers to the identification of irregularities or deviations from established patterns in the behavior or interactions of entities that are not human, such as bots, automated systems, or artificial intelligence agents. NHIs can operate within various digital ecosystems, and their actions can often be monitored through data analytics. Anomalies may indicate potential issues such as security threats, system malfunctions, or unexpected behavior in automated processes. For instance, if a bot typically interacts with users at regular intervals but suddenly starts sending a high volume of messages in a short period, this could be flagged as an anomaly. Effective anomaly detection involves utilizing machine learning algorithms and statistical methods to analyze historical data, establish a baseline of normal behavior, and flag instances that fall outside this baseline. The goal is to ensure the integrity of systems and to take proactive measures against potential risks associated with NHIs.

Examples

  • A bot that suddenly starts sending spam messages at an accelerated rate.
  • An automated trading system that executes a series of trades that deviate significantly from its usual patterns.

Additional Information

  • Anomaly detection techniques include supervised and unsupervised learning, statistical tests, and clustering methods.
  • Timely detection of anomalies can help mitigate risks, enhance security, and improve the overall reliability of systems involving NHIs.

References