What distinguishes anomaly-based detection from signature-based detection?

Anomaly-based detection is characterized by its ability to identify deviations from a baseline of normal behavior within a system or network. This method establishes what is considered "normal" through continuous monitoring and then alerts when it detects behavior that deviates from this established norm. This is particularly effective for recognizing new or unknown threats that do not have predefined signatures, allowing for dynamic threat detection in environments that can change rapidly.

In contrast, signature-based detection relies on a library of known patterns, or signatures, that represent known threats. This form of detection is unable to recognize unfamiliar or novel attacks, as it only identifies what has already been documented in its database. Consequently, anomaly-based detection provides a broader range of threat identification since it focuses on variations from expected behavior rather than just known patterns.

The other options involve aspects that are not specifically distinguishing features of anomaly-based detection. For instance, identifying known patterns is the hallmark of signature-based detection. While it's true that anomaly-based systems can be designed with or without specific security policies, this does not inherently differentiate them from signature detection methods. Additionally, while some anomaly-based systems may block traffic upon detection, this is not a defining characteristic of such systems, as response mechanisms can vary widely across different security solutions.