Network-based anomaly intrusion detection with numeric-and-nominal mixed data

Abstract

Anomaly detection is a key element of intrusion detection systems and a necessary complement of widely used misuse intrusion detection systems. Data sources used by network intrusion detection, like network packets or connections, often contain both numeric and nominal features. Both of these features contain important information for intrusion detection. These two features, on the other hand, have different characteristics. This paper presents a new network based anomaly intrusion detection approach that works well by building profiles for numeric and nominal features in different ways. During training, for each numeric feature, a normal profile is build through statistical distribution inference and parameter estimation, while for each nominal feature, a normal profile is setup through statistical method. These profiles are used as detection models during testing to judge whether a data being tested is benign or malicious. Experiments with the data set of 1999 DARPA (defense advanced research project agency) intrusion detection evaluation show that this approach can detect attacks effectively.