本报告是在对41686起安全事件进行分析的基础上编写的,其中2,013起是数据泄露事件。我们将了解多年来安全事件是如何变化,或没有变化的,并深入了解整体威胁状况以及违规行为的参与者、行动和资产。这将为为读者提供了一种方法来分析违规行为,并在已经熟悉的事件分类模式之外的共同点。2019年数据泄露调查报告 Introduction The statements you will read in the pages that follow are data-driven, either by the incident corpus that is the foundation of this publication, or by non-incident data sets contributed by several security vendors. This report is built upon analysis of 41,686 security incidents, of which 2,013 were confirmed data breaches. We will take a look at how results are changing (or not) over the years as well as digging into the overall threat landscape and the actors, actions, and assets that are present in breaches. Windows into the most common pairs of threat actions and affected assets also are provided. This affords the reader with yet another means to analyze breaches and to find commonalities above and beyond the incident classification patterns that you may already be acquainted with. Fear not, however. The nine incident classification patterns are still around, and we continue to focus on how they correlate to industry. In addition to the nine primary patterns, we have created a subset of data to pull out financially-motivated social engineering (FMSE) attacks that do not have a goal of malware installation. 【更多详情,请下载:2019年数据泄露调查报告】 镝数聚dydata,pdf报告,小数据,可视数据,表格数据
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    2019年数据泄露调查报告

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    价格免费
    年份2019
    来源威瑞森电信
    数据类型数据报告
    关键字数据安全, 数据泄露, 违规行为, 黑客攻击
    店铺镝数进入店铺
    发布时间2019-08-02
    PDF下载

    数据简介

    本报告是在对41686起安全事件进行分析的基础上编写的,其中2,013起是数据泄露事件。我们将了解多年来安全事件是如何变化,或没有变化的,并深入了解整体威胁状况以及违规行为的参与者、行动和资产。这将为为读者提供了一种方法来分析违规行为,并在已经熟悉的事件分类模式之外的共同点。

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    2019年数据泄露调查报告
    
    Introduction
    The statements you will read in the pages that follow are data-driven, either by the incident corpus that is the foundation of this publication, or by non-incident data sets contributed by several security vendors.
    This report is built upon analysis of 41,686 security incidents, of which 2,013 were confirmed data breaches. We will take a look at how results are changing (or not) over the years as well as digging into the overall threat landscape and the actors, actions, and assets that are present in breaches. Windows into the most common pairs of threat actions and affected assets also are provided. This affords the reader with yet another means to analyze breaches and to find commonalities above and beyond the incident classification patterns that you may already be acquainted with.
    Fear not, however. The nine incident classification patterns are still around, and we continue to focus on how they correlate to industry. In addition to the nine primary patterns, we have created a subset of data to pull out financially-motivated social engineering (FMSE) attacks that do not have a goal of malware installation.
    
    【更多详情,请下载:2019年数据泄露调查报告】

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    *本报告来自网络,如有侵权请联系删除
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