2021年数据泄露调查报告相关数据报告_免费下载-镝数聚dydata,相关行业数据商务车,财务风险详细分析,管理水平,薪酬水平,卡车,经营分析2019年数据泄露调查报告 Introduction Thestatementsyouwillreadinthepagesthatfollowaredata-driven,eitherbytheincidentcorpusthatisthefoundationofthispublication,orbynon-incidentdatasetscontributedbyseveralsecurityvendors. Thisreportisbuiltuponanalysisof41,686securityincidents,ofwhich2,013wereconfirmeddatabreaches.Wewilltakealookathowresultsarechanging(ornot)overtheyearsaswellasdiggingintotheoverallthreatlandscapeandtheactors,actions,andassetsthatarepresentinbreaches.Windowsintothemostcommonpairsofthreatactionsandaffectedassetsalsoareprovided.Thisaffordsthereaderwithyetanothermeanstoanalyzebreachesandtofindcommonalitiesaboveandbeyondtheincidentclassificationpatternsthatyoumayalreadybeacquaintedwith. Fearnot,however.Thenineincidentclassificationpatternsarestillaround,andwecontinuetofocusonhowtheycorrelatetoindustry.Inadditiontothenineprimarypatterns,wehavecreatedasubsetofdatatopulloutfinancially-motivatedsocialengineering(FMSE)attacksthatdonothaveagoalofmalwareinstallation. 【更多详情,请下载:2019年数据泄露调查报告】
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    "2021年数据泄露调查报告"相关数据

    更新时间:2024-10-29
    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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