What type of analysis method combines machine learning algorithms and statistical analyses to identify deviations from normal behavior?

The method that combines machine learning algorithms and statistical analyses to identify deviations from normal behavior is behavioral analysis. This approach is especially valuable in cybersecurity, where detecting anomalies is crucial for identifying potential threats and malicious activities.

Behavioral analysis leverages machine learning models to process large volumes of data and recognize patterns associated with normal user or system behavior. By establishing a baseline of what is considered "normal," any significant variation from this baseline can be flagged for further investigation. This capability is essential in identifying threats that traditional signature-based detection methods may miss, such as previously unseen malware or insider threats.

In contrast, quantitative analysis focuses primarily on numerical data and statistical techniques without necessarily incorporating the machine learning algorithms that enhance the ability to detect behavior-based anomalies. Trend analysis looks at changes over time and can identify patterns but does not inherently analyze deviations from a defined normal behavior. Statistical analysis typically involves manipulating data to derive insights or confirm hypotheses but may not apply machine learning techniques to the same extent as behavioral analysis.

Thus, behavioral analysis stands out as the method that uniquely combines the strengths of both machine learning and statistical analysis to identify unusual behaviors and potential security threats effectively.