Anomaly-based detection methods use artificial intelligence and machine learning to create and continually refine a baseline model of normal network activity. The IPS compares ongoing network activity to the model and responds when it finds deviations, like a process that uses more bandwidth than typical or a device that opens a port that's usually closed.
Because anomaly-based IPSs respond to any abnormal behavior, they can often block brand-new cyberattacks that might evade signature-based detection. They can even detect zero-day exploits—attacks that take advantage of software vulnerabilities before the software developer knows about them or has time to patch them.
However, anomaly-based IPSs may be more prone to false positives. Even benign activity, such as an authorized user accessing a sensitive network resource for the first time, can trigger an anomaly-based IPS. As a result, authorized users could be booted from the network or have their IP addresses blocked.