What does QRadar's anomaly detection aim to identify?

QRadar's anomaly detection specifically focuses on identifying unusual patterns or activities that deviate from established baselines within the monitored environment. This capability is crucial because it allows security teams to detect potential threats that may not be evident through conventional security measures or predefined rules.

By understanding what normal behavior looks like in a given environment, QRadar can flag activities that are atypical, signaling potential security incidents or malicious actions. For instance, if a user typically accesses certain files during business hours and suddenly begins making requests at unusual times or accessing a different set of resources, this behavior could be flagged as anomalous and warrant further investigation. This detection mechanism leverages the power of machine learning and statistical analysis to continuously evolve and adapt to the changing patterns within the network.

In contrast, other options such as identifying security policy violations, logging all user attempts, or detecting network traffic spikes may be elements of security monitoring, but they do not encapsulate the core function of anomaly detection, which is specifically to pinpoint deviations from established behavioral baselines.