FRACTAL METALLIC NANOSTRUCTURE AND METHOD OF SYNTHESIS OF FRACTAL METALLIC NANOSTRUCTURE

    公开(公告)号:US20240181569A1

    公开(公告)日:2024-06-06

    申请号:US18385560

    申请日:2023-10-31

    CPC classification number: B23K26/362 B33Y40/20 B33Y80/00

    Abstract: A method for synthesis of fractal metallic nanostructure is provided. The method includes applying electron-beam irradiation directly on a metal-containing carbon nanosheet. Moreover, the Disclosed Invention relates to a novel method for the direct synthesis and control of fractal growth of metallic nanostructures on hybrid self-assembled metal/carbon nanosheets using electron-beam irradiation. In particular, the nanosheet is the source or the metallic precursor for the fractal nanostructure as well as the substrate on which the fractal formation takes place. In addition, the irradiation-induced interactions between the electron-beam and the carbon-based nanosheet enables the patterning and carving of the nanosheet with different designs of various complexities to eventually control the path and route of the irradiation induced nucleation and growth of the metallic fractal morphology.

    Anomalous user account detection systems and methods

    公开(公告)号:US11991196B2

    公开(公告)日:2024-05-21

    申请号:US17685687

    申请日:2022-03-03

    CPC classification number: H04L63/1425 H04L63/0876

    Abstract: Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns. Most existing approaches typically build models by reconstructing single-day and individual-user behaviors. However, without capturing long-term signals and group-correlation signals, the models cannot identify low-signal yet long-lasting threats, and will incorrectly report many normal users as anomalies on busy days, which, in turn, leads to a high false positive rate. A method is provided based on compound behavior, which takes into consideration long-term patterns and group behaviors. The provided method leverages a novel behavior representation and an ensemble of deep autoencoders and produces an ordered investigation list.

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