A reinforcement learning approach for attack graph analysis

Mehdi Yousefi; Nhamo Mtetwa; Yan Zhang; Huaglory Tianfield

doi:10.1109/TrustCom/BigDataSE.2018.00041

A reinforcement learning approach for attack graph analysis

Mehdi Yousefi, Nhamo Mtetwa, Yan Zhang, Huaglory Tianfield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

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.

Original language	English
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)
Publisher	IEEE
Pages	212-217
Number of pages	6
ISBN (Electronic)	9781538643884
ISBN (Print)	9781538643891
DOIs	https://doi.org/10.1109/TrustCom/BigDataSE.2018.00041
Publication status	Published - 6 Sep 2018

Publication series

Name
ISSN (Electronic)	2324-9013

Keywords

cyber security
reinforcement learning
Q-learning
attack graph

Documents and Links

10.1109/TrustCom/BigDataSE.2018.00041

Yousefi, M. et al (2018) A Reinforcement Learning Approach for Attack Graph Analysis
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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

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title = "A reinforcement learning approach for attack graph analysis",

abstract = "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.",

keywords = "cyber security, reinforcement learning, Q-learning, attack graph",

author = "Mehdi Yousefi and Nhamo Mtetwa and Yan Zhang and Huaglory Tianfield",

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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)., Track 2: Security Track 1, IEEE, pp. 212-217. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00041

A reinforcement learning approach for attack graph analysis. / Yousefi, Mehdi; Mtetwa, Nhamo; Zhang, Yan ; Tianfield, Huaglory.

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). IEEE, 2018. p. 212-217 Track 2: Security Track 1.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Yousefi M, Mtetwa N, Zhang Y , Tianfield H. 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). IEEE. 2018. p. 212-217. Track 2: Security Track 1 https://doi.org/10.1109/TrustCom/BigDataSE.2018.00041

A reinforcement learning approach for attack graph analysis

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