A rough set theory based method for anomaly intrusion detection in computer network systems

Abstract

Abstract: Intrusion detection is important in the defense‐in‐depth network security framework. This paper presents an effective method for anomaly intrusion detection with low overhead and high efficiency. The method is based on rough set theory to extract a set of detection rules with a minimal size as the normal behavior model from the system call sequences generated during the normal execution of a process. It is capable of detecting the abnormal operating status of a process and thus reporting a possible intrusion. Compared with other methods, the method requires a smaller size of training data set and less effort to collect training data and is more suitable for real‐time detection. Empirical results show that the method is promising in terms of detection accuracy, required training data set and efficiency.