- 专利标题: MALWARE DETECTION USING LOCAL COMPUTATIONAL MODELS
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申请号: US15657379申请日: 2017-07-24
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公开(公告)号: US20190026466A1公开(公告)日: 2019-01-24
- 发明人: Sven Krasser , David Elkind , Patrick Crenshaw , Kirby James Koster
- 申请人: CrowdStrike, Inc.
- 主分类号: G06F21/56
- IPC分类号: G06F21/56 ; G06F21/55
摘要:
Example techniques herein determine that a trial data stream is associated with malware (“dirty”) using a local computational model (CM). The data stream can be represented by a feature vector. A control unit can receive a first, dirty feature vector (e.g., a false miss) and determine the local CM based on the first feature vector. The control unit can receive a trial feature vector representing the trial data stream. The control unit can determine that the trial data stream is dirty if a broad CM or the local CM determines that the trial feature vector is dirty. In some examples, the local CM can define a dirty region in a feature space. The control unit can determine the local CM based on the first feature vector and other clean or dirty feature vectors, e.g., a clean feature vector nearest to the first feature vector.
公开/授权文献
- US10726128B2 Malware detection using local computational models 公开/授权日:2020-07-28
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