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Building a Machine Learning model without compromising data privacy

机译:在不影响数据隐私的情况下构建机器学习模型

摘要

Systems and methods include obtaining file identifiers associated with files in production data; obtaining lab data from one or more public repositories of malware samples based on the file identifiers for the production data; and utilizing the lab data for training a machine learning process for classifying malware in the production data. The obtaining file identifiers can be based on monitoring of users associated with the files, and only the file identifiers are maintained based on the monitoring. The lab data can include samples from the one or more public repositories matching the corresponding file identifiers for the production data. The lab data can include samples from the one or more public repositories that have features closely related to features of the production data.
机译:系统和方法包括获取与生产数据中的文件相关联的文件标识符; 根据生产数据的文件标识符,从恶意软件样本的一个或多个公共存储库获取实验室数据; 利用实验室数据培训用于对生产数据中的恶意软件进行分类的机器学习过程。 获取文件标识符可以基于对与文件关联的用户的监视,并且仅基于监视维护文件标识符。 实验室数据可以包括来自一个或多个公共存储库的样本,该公共存储库匹配生产数据的相应文件标识符。 实验室数据可以包括来自一个或多个公共存储库的示例,该公共存储库具有与生产数据的特征密切相关的功能。

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