Attack graph approach is a common tool for the analysis of network security. However, analysis of attack graphs could be complicated and difficult depending on the attack graph size. This paper presents an approximate analysis approach for attack graphs based on Q-learning. First, we employ multi-host multi-stage vulnerability analysis (MulVAL) to generate an attack graph for a given network topology. Then we refine the attack graph and generate a simplified graph called a transition graph. Next, we use a Q-learning model to find possible attack routes that an attacker could use to compromise the security of the network. Finally, we evaluate the approach by applying it to a typical IT network scenario with specific services, network configurations, and vulnerabilities.
|Title of host publication||2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)|
|Number of pages||6|
|Publication status||Published - 6 Sep 2018|
- cyber security
- reinforcement learning
- attack graph
Yousefi, M., Mtetwa, N., Zhang, Y., & Tianfield, H. (2018). A reinforcement learning approach for attack graph analysis. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 212-217). [Track 2: Security Track 1] IEEE. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00041