Anomaly detection, or outlier detection, is the identification of data points, observations, or events that do not conform to expected patterns of a given group. Anomalies or outliers occur very infrequently but can signify a large and significant threat, such as cyber intrusion, financial fraud, compliance violation, and machinery malfunction, to businesses. Anomaly detection has traditionally relied on subject matter experts to curate and set business rules to trigger red flags in data. This traditional approach is inherently flawed because:
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The Impact of Deep Learning on Anomaly Detection | Legaltech News - Law.com
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