A Markov Chain Model of Temporal Behavior for Anomaly Detection
Nong Ye · 2000
This paper presents an anomaly detection technique to detect intrusions into computer and network systems. In this technique, a Markov chain model is used to represent a temporal profile of normal behavior in a computer and network system. The Markov chain model of the norm profile is learned from historic data of the system’s normal behavior. The observed behavior of the system is analyzed to infer the probability that the Markov chain model of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The technique was implemented and tested on the audit data of a Sun Solaris system. The testing results showed that the technique clearly distinguished intrusive activities from normal activities in the testing data.