Impact-Driven Sampling Strategies for Hybrid Attack Graphs

Conference · Sun Jan 29 23:00:00 EST 2023

DOI:https://doi.org/10.1109/HST56032.2022.10025439· OSTI ID:1964163

Subasi, Omer ^[1]; Purohit, Sumit ^[1]; Bhattacharya, Arnab ^[1]; Chatterjee, Samrat ^[1]

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1. BATTELLE (PACIFIC NW LAB)

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graph (HAG) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. We propose a dimension reduction via property-preserving multi-layer graph sampling algorithms. Existing property-preserving graph sampling approaches generate a representative subgraph of an original large-sized graph while preserving the key properties, such as node and edge distribution, clustering coefficients, and betweenness. On the other hand, we propose impact-driven sampling strategies to transform the input data to a lower-dimensional representation while retaining key properties of the data.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1964163
• Report Number(s): PNNL-SA-178629
• Country of Publication: United States
• Language: English