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PE METHOD AND SYSTEM FOR DETECTING MALWARE GENERATING DISTRORTION-FREE IMAGE OF PE OPCODE FOR AI LEARNING

机译:用于检测用于学习的pe操作码的恶意软件的无失真图像的pe方法和系统

摘要

A malicious code detection method for generating an image without distortion of malicious code for artificial intelligence learning, comprising the step of generating a learning model, and determining whether an executable file is a malicious code using the learning model, the The step of generating the learning model includes generating a first opcode sequence for a pre-stored normal executable file, generating a second opcode sequence for a pre-stored malware executable file, and using a pre-stored map table. Thus, generating an image file for each of the first and second opcode sequences, and refining the image file for the first opcode sequence and the image file for the second opcode sequence There is provided a method for detecting malicious codes including generating a learning model.
机译:一种用于在不破坏用于人工智能学习的恶意代码的情况下生成图像的恶意代码检测方法,包括以下步骤:生成学习模型,并使用该学习模型确定可执行文件是否为恶意代码。该模型包括为预存储的普通可执行文件生成第一操作码序列,为预存储的恶意软件可执行文件生成第二操作码序列以及使用预存储的映射表。因此,为第一和第二操作码序列中的每一个生成图像文件,并且细化用于第一操作码序列的图像文件和用于第二操作码序列的图像文件。提供了一种用于检测恶意代码的方法,该方法包括生成学习模型。

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