A Fusion Model for Network Threat Identification and Risk Assessment

Abstract

Current practice for real-time security risk assessment typically takes intrusion detection systems alerts as the only source of risk factor. Their assessment results are more likely to suffer from the impact of false positive alerts in the increasingly complex and severe network security environment. This paper proposes a novel online fusion model for dynamical network risk assessment by using multiple risk factors. The model is composed by three fusion levels. First, an online alert fusion algorithm is proposed and the redundancy of the raw alerts is dramatically reduced. Then, the model employs Dempster-Shafer theory to handle uncertainties and ignorance existed in the multiple risk factors. Threats in different kinds of severity levels are identified. Finally, the whole network risk distribution is dynamically calculated and reported by using HMM approach. Experiments show the effectiveness and validity of our method.