Rule-Based Detection

B.

Rule-based detection and statistical detection are two different methods used in cybersecurity for identifying potential security threats.

Rule-based detection involves using predefined rules or signatures to identify known patterns of malicious behavior. These rules are created based on known attack methods, vulnerabilities, and indicators of compromise. For example, a rule-based detection system may search for specific keywords or patterns in network traffic or monitor for specific types of file extensions being downloaded. If the system identifies a match with any of these predefined rules, it triggers an alert or takes appropriate action.

On the other hand, statistical detection involves using statistical models to identify anomalous behavior that may be indicative of a security threat. Statistical models use machine learning algorithms to analyze large volumes of data, learn patterns, and identify outliers. These models can adapt over time as they learn more about the environment and can detect previously unknown threats.

In summary, rule-based detection relies on predefined rules or signatures to identify known threats, while statistical detection uses machine learning to identify anomalous behavior that may be indicative of a security threat. Both methods have their strengths and weaknesses, and a combination of the two is often used for effective threat detection and prevention.