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We compared the grain and grain boundary (GB) nanostructures in two Ba122 tapes with similarly highJ_c. The Ag-sheathed tape made by hot pressing has larger, more plate-like grains with betterc-axis alignment but has more GBs blocked by FeAs and Ba–O. In contrast, the tape made by cold pressing with an Ag-Sn/stainless steel sheath possesses fewer plate-like grains and weaker grain alignment but has more continuous current paths with clean physically well-connected GBs. Our nanostructural comparison emphasizes the strong need to achieve both good grain alignment and clean GBs for furtherJ_cimprovement of Ba122 tapes.

 

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 article, 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 as good, or better, than every state-of-the-art approach to anomalous link detection and prediction that we tested. As anomaly-based intrusion detection systems, our models efficiently identified anomalous connections between entities with high precision and outperformed all other unsupervised techniques for anomalous lateral movement detection. Additionally, we show that as a piece of a larger anomaly detection pipeline, Euler models perform well enough for use in real-world systems. With more advanced, yet still lightweight, alerting mechanisms ingesting the embeddings produced by Euler models, precision is boosted from 0.243, to 0.986 on real-world network traffic.