How does signature-based detection work in an IDS?

Signature-based detection in an Intrusion Detection System (IDS) functions by comparing network activity against a database of known attack patterns or signatures. This method relies on previously identified threats; when the IDS detects an activity that matches one of these known signatures, it can trigger an alert indicating a potential attack.

The efficacy of this approach lies in its ability to quickly identify and respond to established threats, as it uses a library of documented attack patterns which are regularly updated. This makes signature-based detection particularly effective for identifying well-documented and common types of attacks. However, it may struggle with detecting zero-day vulnerabilities or new, unknown attacks that do not match any of the existing signatures in its database.

In contrast, the other options describe different methodologies: analyzing user behavior pertains to behavior-based detection, dynamically learning from system changes relates to machine-learning techniques, and aggregating data from multiple sources is associated with broader threat intelligence practices. Each of these approaches addresses cybersecurity threats from a different angle, but they do not define the mechanics of signature-based detection.