Isaiah J. King (The George Washington University), H. Howie Huang (The George Washington University)

Lateral movement is a key stage of system compromise used by advanced persistent threats. Detecting it is no simple task. When network host logs are abstracted into discrete temporal graphs, the problem can be reframed as anomalous edge detection in an evolving network. Research in modern deep graph learning techniques has produced many creative and complicated models for this task. However, as is the case in many machine learning fields, the generality of models is of paramount importance for accuracy and scalability during training and inference. In this paper, we propose a formalized approach to this problem with a framework we call Euler. It consists of a model-agnostic graph neural network stacked upon a model-agnostic sequence encoding layer such as a recurrent neural network. Models built according to the Euler framework can easily distribute their graph convolutional layers across multiple machines for large performance improvements. Additionally, we demonstrate that Euler-based models are competitive, or better than many state-of-the-art approaches to anomalous link detection and prediction. As anomaly-based intrusion detection systems, Euler models can efficiently identify anomalous connections between entities with high precision and outperform other unsupervised techniques for anomalous lateral movement detection.

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Bingyong Guo (Institute of Software, Chinese Academy of Sciences), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhenliang Lu (The University of Sydney), Qiang Tang (The University of Sydney), jing xu (Institute of Software, Chinese Academy of Sciences), Zhenfeng Zhang (Institute of Software, Chinese Academy of Sciences)

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Douglas Everson (Clemson University), Long Cheng (Clemson University), and Zhenkai Zhang (Clemson University)

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Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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