What analysis focuses on infrequent, anomalous events for identifying suspicious behavior?

The correct answer is long-tail analysis, which is specifically geared toward identifying infrequent or anomalous events within a dataset. This analysis recognizes that while certain events may occur often and can be considered normal, there are many less frequent occurrences that might indicate suspicious behavior. In cybersecurity, these rare events can be more telling of a potential threat or breach, as attackers often employ uncommon tactics to evade detection.

Long-tail analysis effectively highlights these anomalies, allowing security analysts to focus on patterns or events that do not conform to the expected behavior. This approach can provide crucial insights that might otherwise be overlooked in a system filled with frequent occurrences of common events.

Behavioral analytics, while related, generally analyzes user behavior over time and establishes baselines to identify deviations. It may not target specifically the infrequent events as sharply as long-tail analysis does. Triggering SOAR playbooks in Enterprise Security refers to automated responses based on predefined criteria but does not inherently involve the identification of infrequent events. Outlier detection is a technique used in statistics to identify points that differ significantly from the rest of the data but is not typically labeled in the context of infrequent events in the same way as long-tail analysis is.