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    FRACTAL METALLIC NANOSTRUCTURE AND METHOD OF SYNTHESIS OF FRACTAL METALLIC NANOSTRUCTURE
    6.
    发明公开
    FRACTAL METALLIC NANOSTRUCTURE AND METHOD OF SYNTHESIS OF FRACTAL METALLIC NANOSTRUCTURE 审中-公开

    公开(公告)号：US20240181569A1

    公开(公告)日：2024-06-06

    申请号：US18385560

    申请日：2023-10-31

    申请人： Qatar Foundation for Education, Science and Community Development

    发明人： Hicham Hamoudi ,  Sara Iyad Mohd Saleh Ahmad

    IPC分类号： B23K26/362 ,  B33Y40/20 ,  B33Y80/00

    CPC分类号： B23K26/362 ,  B33Y40/20 ,  B33Y80/00

    摘要： 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
    7.
    发明授权
    Anomalous user account detection systems and methods 有权

    公开(公告)号：US11991196B2

    公开(公告)日：2024-05-21

    申请号：US17685687

    申请日：2022-03-03

    申请人： Qatar Foundation for Education, Science and Community Development

    发明人： Issa M. Khalil ,  Ting Yu ,  Eui J. Choo ,  Lun-Pin Yuan ,  Sencun Zhu

    IPC分类号： H04L9/40

    CPC分类号： H04L63/1425 ,  H04L63/0876

    摘要： 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.

    TOR-BASED MALWARE DETECTION
    8.
    发明公开
    TOR-BASED MALWARE DETECTION 审中-公开

    公开(公告)号：US20240154997A1

    公开(公告)日：2024-05-09

    申请号：US18387937

    申请日：2023-11-08

    申请人： Qatar Foundation for Education, Science and Community Development

    发明人： Priyanka Dodia ,  Mashael Al Sabah

    IPC分类号： H04L9/40 ,  G06N20/00

    CPC分类号： H04L63/145 ,  G06N20/00 ,  H04L63/1425

    摘要： A machine learning model for classifying encrypted traffic as benign or malicious without having to decrypt the traffic is provided that used traffic patterns from network logs to classify the traffic based on learned patterns for malware, and is capable of identifying zero-day malware is provided via: extracting encrypted traffic from communication logs for a network; identifying, from the encrypted traffic, while still encrypted, traffic patterns for users of the network; and classifying, via a machine learning model, the encrypted traffic as benign traffic or malicious traffic without decrypting the encrypted traffic according to the traffic patterns identified.