Which type of intrusion detection system uses statistical analysis to identify potential intrusions?

Anomaly-based intrusion detection systems (IDS) are designed to identify potential intrusions by establishing a baseline of normal network behavior and then using statistical analysis to detect deviations from this baseline. The system monitors network traffic, user behavior, and system activities, comparing them to predefined models of normal operations.

When it notices significant deviations or anomalies, it raises alerts indicating that potential suspicious activity may be occurring. This approach is beneficial for identifying new or unknown attacks that signature-based systems might miss since it focuses on the behavior rather than specific known attack patterns.

In contrast, signature-based systems rely on predefined signatures of known threats and are effective in detecting established attack patterns but may struggle with novel threats that do not match any existing signatures. Hybrid IDS combines elements of both anomaly and signature-based detection, while network-based systems refer to the deployment area rather than the method of detection. Thus, anomaly-based systems are particularly valuable for their capability to detect unusual patterns that signal potential intrusions.