公开(公告)号：US20140304197A1

公开(公告)日：2014-10-09

申请号：US13326194

申请日：2011-12-14

Applicant: Sumesh Jaiswal ,  Ashish Aggarwal ,  Phillip DiCorpo ,  Shitalkumar S. Sawant ,  Sally Kauffman ,  Alan Dale Galindez

Inventor： Sumesh Jaiswal ,  Ashish Aggarwal ,  Phillip DiCorpo ,  Shitalkumar S. Sawant ,  Sally Kauffman ,  Alan Dale Galindez

IPC: G06F15/18

CPC classification number: G06F21/6209 ,  G06F2221/2101 ,  G06F2221/2141 ,  G06F2221/2147 ,  H04L63/20

Abstract: A computing device receives a document that was incorrectly classified as sensitive data based on a machine learning-based detection (MLD) profile. The computing device modifies a training data set that was used to generate the MLD profile by adding the document to the training data set as a negative example of sensitive data to generate a modified training data set. The computing device then analyzes the modified training data set using machine learning to generate an updated MLD profile.

Abstract translation: 计算设备基于机器学习检测（MLD）简档接收被错误地分类为敏感数据的文档。 计算设备通过将文档添加到训练数据集作为敏感数据的负面示例来修改用于生成MLD简档的训练数据集，以生成修改的训练数据集。 然后，计算设备使用机器学习来分析修改的训练数据集，以生成更新的MLD轮廓。

公开(公告)号：US20120303558A1

公开(公告)日：2012-11-29

申请号：US13191018

申请日：2011-07-26

Applicant: Sumesh Jaiswal

Inventor： Sumesh Jaiswal

IPC: G06F15/18

CPC classification number: G06N99/005

Abstract: A computer-implemented method may include (1) identifying a plurality of specific categories of sensitive information to be protected by a DLP system, (2) obtaining a training data set for each specific category of sensitive information that includes a plurality of positive and a plurality of negative examples of the specific category of sensitive information, (3) using machine learning to train, based on an analysis of the training data sets, at least one machine learning-based classifier that is capable of detecting items of data that contain one or more of the plurality of specific categories of sensitive information, and then (4) deploying the machine learning-based classifier within the DLP system to enable the DLP system to detect and protect items of data that contain one or more of the plurality of specific categories of sensitive information in accordance with at least one DLP policy of the DLP system.

Abstract translation: 计算机实现的方法可以包括（1）识别要由DLP系统保护的多个特定类别的敏感信息，（2）获得针对每个特定类别的敏感信息的训练数据集，其包括多个正的和 多个敏感信息的特定类别的负面例子，（3）使用机器学习训练，基于训练数据集的分析，至少一个基于机器学习的分类器能够检测包含一个 或多个特定类别的敏感信息，然后（4）在DLP系统内部署基于机器学习的分类器，以使DLP系统能够检测和保护包含多个特定类别中的一个或多个的数据项 根据DLP系统的至少一个DLP策略的敏感信息类别。