Exploring Attack Graphs for Security Risk Assessment: A Probabilistic Approach
The attack graph methodology can be used to identify the potential attack paths that an attack can propagate. A risk assessment model based on Bayesian attack graph is presented in this paper. Firstly, attack graphs are generated by the MULVAL (Multi-host, Multistage Vulnerability Analysis) tool according to sufficient information of vulnerabilities, network configurations and host connectivity on networks. Secondly, the probabilistic attack graph is established according to the causal relationships among sophisticated multi-stage attacks by using Bayesian Networks. The probability of successful exploits is calculated by combining index of the Common Vulnerability Scoring System, and the static security risk is assessed by applying local conditional probability distribution tables of the attribute nodes. Finally, the overall security risk in a small network scenario is assessed. Experimental results demonstrate our work can deduce attack intention and potential attack paths effectively, and provide effective guidance on how to choose the optimal security hardening strategy.
Key wordsrisk assessment attack graph Bayesian networks prior probability
CLC numberTP 393
Unable to display preview. Download preview PDF.
- Ou X, Homer J, Zhang S, et al. MulVal project at Kansas State University[EB/OL]. [2013-11-20]. http://people.cs.ksu. edu/~xou/mulval/.Google Scholar
- Jajodia S, Noel S. Topological Vulnerability Analysis: A Powerful New Approach for Network Attack Prevention, Detection, and Response [M]. Singapore: World Scientific Publishing Company, 2008.Google Scholar
- Ou X, Boyer W F, McQueen M A. A scalable approach to attack graph generation[C]//Proc 13th ACM Conference on Computer and Communications Security (CCS 2006). New York: ACM, 2006: 336–345.Google Scholar
- Xie P, Li J, Ou X, et al. Using Bayesian networks for cyber security analysis[C] //Proc 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). Washington D C: IEEE, 2010: 211–220.Google Scholar
- Chen X J, Fang B X, Tan Q F, et al. Inferring attack intent of malicious insider based on probabilistic attack graph model[J]. Chinese Journal of Computers, 2014, 37(1):62–72(Ch).Google Scholar
- National Institute of Standards and Technology (NIST). National vulnerability database(NVD)[EB/OL]. [2017-03-20]. https://nvd.nist. gov/.Google Scholar
- The Forum of Incident Response and Security Teams (FIRST). Common vulnerability scoring system (CVSS) [EB/OL]. [2017-07-24]. https://www.first.org/cvss/.Google Scholar
- AT&T Labs Research. GraphViz-graph visualization software[EB/OL]. [2017-08-06]. http://www.graphviz.org/.Google Scholar