Using Bayesian networks to fuse intrusion evidences and detect zero-day attack paths

Xiaoyan Sun, Jun Dai, Peng Liu, Anoop Singhal, John Yen

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


This chapter studies the zero-day attack path identification problem. Detecting zero-day attacks is a fundamental challenge faced by enterprise network security defense. A multi-step attack involving one or more zero-day exploits forms a zero-day attack path. This chapter describes a prototype system called ZePro, which takes a probabilistic approach for zero-day attack path identification. ZePro first constructs a network-wide system object instance graph by parsing system calls collected from all hosts in the network, and then builds a Bayesian network on top of the instance graph. The instance-graph-based Bayesian network is able to incorporate the collected intrusion evidence and infer the probabilities of object instances being infected. By connecting the instances with high probabilities, ZePro is able to generate the zero-day attack paths. This chapter evaluated the effectiveness of ZePro for zero-day attack path identification.

Original languageEnglish (US)
Title of host publicationNetwork Security Metrics
PublisherSpringer International Publishing
Number of pages21
ISBN (Electronic)9783319665054
ISBN (Print)9783319665047
StatePublished - Nov 15 2017

All Science Journal Classification (ASJC) codes

  • General Computer Science


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