Using Vulnerability Analysis to Model Attack Scenario for Collaborative Intrusion Detection

Abstract

Intrusion detection is an important part of network security protection. Traditional intrusion detection systems (IDSs) only focus on low-level attacks and raise alerts independently, though there may be logical connections between them. At the same time, the amount of alerts becomes unmanageable including actual alerts mixed with false alerts. Therefore, improved techniques are needed. The general idea is to introduce collaboration achieved by taking advantage of vulnerability analysis as contextual information and thus enable IDSs to correctly identify successful attacks while simultaneously reducing the number of false positives and providing a stronger validation attack scenario. In particular, with the verification pattern with precondition and effect of successful attack and necessary context (mainly modeled as host and connectivity information), the architecture that proposes in this paper can reduce the false alert rate and identify true alerts corresponding to successful attacks to construct attack scenario. Through the experimental results with DARPA Data Sets 2000 from Lincoln laboratory and the Treasure Hunt Dataset, it demonstrates the potential of the proposed techniques.