Behavior-based security is a proactive approach to security in which all relevant activity is monitored so that deviations from normal behavior patterns can be identified and dealt with quickly. As machine learning continues to improve, this approach to security management is expected to play an important role in securing computing at the edge of the network.
Traditional security software is signature-oriented: the software monitors data streams and compares data in transit to signatures in an anti-virus vendor's library of known threats. Behavior-based security programs work a little differently -- they monitor data streams too, but then they compare data stream activity to a baseline of normal behavior and look for anomalies. Behavior-based security products use applied mathematics and machine learning to flag events that are statistically significant.
While there may still be instances where an organization needs to choose between signature-based and anomaly-based security software, there is a broad range of intrusion detection and prevention products that combine both approaches.
Advantages of behavior-based security
In general, signature-based tools are best at identifying and repelling known threats, while behavior-based are best for fighting zero-day exploits that have not yet made it onto a list of known threat signatures. Most behavior-based security programs come with a standard set of policies for which behaviors should be allowed and which should be considered suspicious, but also allow administrators to customize policies and create new policies.
Behavior-based security software
Depending upon its capabilities, a behavior-based security software product may be marketed as a network behavior anomaly detection (NBAD) product, a behavior-based intrusion detection product, a behavior threat analysis (BTA) product or a user behavior analytics (UBA) product. Some behavior-security products are sophisticated enough to apply machine learning algorithms to data streams so that security analysts don't need to identify what comprises normal behavior. Other products include behavioral biometrics features that are capable of mapping specific behavior, such as typing patterns, to specific user behavior. Most products have sophisticated correlation engines to minimize the number of alerts and false positives.