Visible to the public Time Independent Security Analysis for Dynamic Networks Using Graphical Security Models

TitleTime Independent Security Analysis for Dynamic Networks Using Graphical Security Models
Publication TypeConference Paper
Year of Publication2018
AuthorsEnoch, S. Yusuf, Hong, J. B., Kim, D. S.
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)
ISBN Number978-1-5386-4388-4
KeywordsAnalytical models, composability, cyber security, Databases, Dynamic Network, Dynamic Networks and Security, Dynamical Systems, graph theory, graphical security model, graphical security models, GSM, Metrics, network systems security, Network topology, pubcrawl, Resiliency, security, security analysis, security models, security of data, Servers, Time independent security analysis, Time-independent Hierarchical Attack Representation Model, Topology
Abstract

It is technically challenging to conduct a security analysis of a dynamic network, due to the lack of methods and techniques to capture different security postures as the network changes. Graphical Security Models (e.g., Attack Graph) are used to assess the security of network systems, but it typically captures a snapshot of a network state to carry out the security analysis. To address this issue, we propose a new Graphical Security Model named Time-independent Hierarchical Attack Representation Model (Ti-HARM) that captures security of multiple network states by taking into account the time duration of each network state and the visibility of network components (e.g., hosts, edges) in each state. By incorporating the changes, we can analyse the security of dynamic networks taking into account all the threats appearing in different network states. Our experimental results show that the Ti-HARM can effectively capture and assess the security of dynamic networks which were not possible using existing graphical security models.

URLhttps://ieeexplore.ieee.org/document/8455957
DOI10.1109/TrustCom/BigDataSE.2018.00089
Citation Keyenoch_time_2018