Visible to the public A Reinforcement Learning Approach for Attack Graph Analysis

TitleA Reinforcement Learning Approach for Attack Graph Analysis
Publication TypeConference Paper
Year of Publication2018
AuthorsYousefi, M., Mtetwa, N., Zhang, Y., Tianfield, H.
Conference Name2018 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)
Keywordsapproximate analysis approach, attack graph, attack graph analysis, attack graph size, Attack Graphs, Big Data, composability, computer network security, computer security, Conferences, control theory, cyber security, data privacy, graph theory, Handheld computers, Internet, learning (artificial intelligence), Metrics, multistage vulnerability analysis, possible attack routes, pubcrawl, q-learning, reinforcement learning, reinforcement learning approach, resilience, Resiliency, security, security of data, simplified graph, transition graph

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.

Citation Keyyousefi_reinforcement_2018