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Generic and static detection of malware installation packages

机译:恶意软件安装包的常规和静态检测

摘要

Systems and methods for generic and static detection of malware using machine learning are provided. According to one embodiment, a computing device receives an executable application or a part thereof. A package name associated with the received application is extracted. The received executable application is classified as being malicious or non-malicious based on evaluation of the package name using a language model. When the received executable application is classified as being non-malicious by the language model, then a further classification process is performed on the received executable application by extracting one or more icons associated with the received executable application. A set of icons of the one or more icons is evaluated using a deep neural network (DNN) model.
机译:提供了使用机器学习对恶意软件进行一般和静态检测的系统和方法。根据一个实施例,一种计算设备接收可执行应用程序或其一部分。提取与接收到的应用程序关联的程序包名称。基于使用语言模型对程序包名称的评估,将接收到的可执行应用程序分为恶意或非恶意。当通过语言模型将接收到的可执行应用程序分类为非恶意时,则通过提取与接收到的可执行应用程序关联的一个或多个图标,对接收到的可执行应用程序执行进一步的分类过程。使用深度神经网络(DNN)模型评估一个或多个图标中的一组图标。

著录项

  • 公开/公告号US10621343B1

    专利类型

  • 公开/公告日2020-04-14

    原文格式PDF

  • 申请/专利权人 FORTINET INC.;

    申请/专利号US201715826969

  • 发明设计人 DAVID MACIEJAK;MINH TRAN;

    申请日2017-11-30

  • 分类号G06F21/56;G06N3/08;G06N3/04;

  • 国家 US

  • 入库时间 2022-08-21 11:30:36

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