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Automated Systems and Methods for Generative Multimodel Multiclass Classification and Similarity Analysis Using Machine Learning

机译:使用机器学习进行生成式多模型多类分类和相似性分析的自动化系统和方法

摘要

Under one aspect, a computer-implemented method includes receiving a query at a query interface about whether a computer file comprises malicious code. It is determined, using at least one machine learning sub model corresponding to a type of the computer file, whether the computer file comprises malicious code. Data characterizing the determination are provided to the query interface. Generating the sub model includes receiving computer files at a collection interface. Multiple sub populations of the computer files are generated based on respective types of the computer files, and random training and testing sets are generated from each of the sub populations. At least one sub model for each random training set is generated.
机译:在一个方面,一种计算机实现的方法包括:在查询界面处接收关于计算机文件是否包括恶意代码的查询。使用至少一个与计算机文件的类型相对应的机器学习子模型来确定计算机文件是否包含恶意代码。表征确定的数据被提供给查询界面。生成子模型包括在收集接口处接收计算机文件。基于计算机文件的各个类型来生成计算机文件的多个子种群,并且从每个子种群中生成随机训练和测试集。为每个随机训练集生成至少一个子模型。

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