KRATO

Rule-based Anomaly Detection: This is already framed by specific set of rules that describe an anomaly and assigns the thresholds and limits. We typically rely on the experience of industry experts whose findings are ideal to detect known anomalies. These anomalies are familiar anomalies to us and we can easily recognize, whether it is normal or abnormal. One of the major flaws of rule-based systems is that they don't detect anomalies automatically as patterns change. To learn new patterns, a new model would have to be built each time.