Ends March 1! GIAC Certification Attempt Included or $400 Off with SANS OnDemand and vLive Training!

IDFAQ: What is behavior based Intrusion Detection?

There are two complementary approaches to detecting intrusions, knowledge-based approaches and behavior-based approaches. This entry describes the second approach. It must be noted that very few tools today implement such an approach, even if the founding Denning paper {D. Denning, An Intrusion Detection Model, IEEE transactions on software engineering} recognizes this as a requirement for IDS systems.

Behavior-based intrusion detection techniques assume that an intrusion can be detected by observing a deviation from normal or expected behavior of the system or the users. The model of normal or valid behavior is extracted from reference information collected by various means. The intrusion detection system later compares this model with the current activity. When a deviation is observed, an alarm is generated. In other words, anything that does not correspond to a previously learned behavior is considered intrusive. Therefore, the intrusion detection system might be complete (i.e. all attacks should be caught), but its accuracy is a difficult issue (i.e. you get a lot of false alarms).

Advantages of behavior-based approaches are that they can detect attempts to exploit new and unforeseen vulnerabilities. They can even contribute to the (partially) automatic discovery of these new attacks. They are less dependent on operating system-specific mechanisms. They also help detect 'abuse of privileges' types of attacks that do not actually involve exploiting any security vulnerability. In short, this is the paranoid approach: Everything which has not been seen previously is dangerous.

The high false alarm rate is generally cited as the main drawback of behavior-based techniques because the entire scope of the behavior of an information system may not be covered during the learning phase. Also, behavior can change over time, introducing the need for periodic online retraining of the behavior profile, resulting either in unavailability of the intrusion detection system or in additional false alarms. The information system can undergo attacks at the same time the intrusion detection system is learning the behavior. As a result, the behavior profile contains intrusive behavior, which is not detected as anomalous.

Herve Debar
IBM Zurich Research Laboratory