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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PMTUD is not Panacea: Revisiting IP Fragmentation Attacks against...

Xuewei Feng (Tsinghua University), Qi Li (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Baojun Liu (Tsinghua University), Xiaofeng Zheng (Institute for Network Sciences and Cyberspace, Tsinghua University; QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.), Qiushi Yang (QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.), Haixin Duan…

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Chunked-Cache: On-Demand and Scalable Cache Isolation for Security Architectures

Ghada Dessouky (Technical University of Darmstadt), Emmanuel Stapf (Technical University of Darmstadt), Pouya Mahmoody (Technical University of Darmstadt), Alexander Gruler (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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