Analyzing Malware Log Data to Support Security Information and Event Management: Some Research Results

Roland Gabriel, Tobias Hoppe, Alexander Pastwa, Sebastian Sowa
{"title":"Analyzing Malware Log Data to Support Security Information and Event Management: Some Research Results","authors":"Roland Gabriel, Tobias Hoppe, Alexander Pastwa, Sebastian Sowa","doi":"10.1109/DBKDA.2009.26","DOIUrl":null,"url":null,"abstract":"Enterprise information infrastructures are generally characterized by a multitude of information systems which support decision makers in fulfilling their duties. The object of information security management is the protection of these systems, whereas security information and event management (SIEM) addresses those information management tasks which focus on the short term handling of events, as well as on the long term improvement of the entire information security architectures. This is carried out based on those data which can be logged and collected within the enterprise information security infrastructure. An especially interesting type of log data is data created by anti-malware software. This paper demonstrates in the context of a project case study that data mining (DM) is a well suited approach to detect hidden patterns in malware data and thus to support SIEM.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBKDA.2009.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

Abstract

Enterprise information infrastructures are generally characterized by a multitude of information systems which support decision makers in fulfilling their duties. The object of information security management is the protection of these systems, whereas security information and event management (SIEM) addresses those information management tasks which focus on the short term handling of events, as well as on the long term improvement of the entire information security architectures. This is carried out based on those data which can be logged and collected within the enterprise information security infrastructure. An especially interesting type of log data is data created by anti-malware software. This paper demonstrates in the context of a project case study that data mining (DM) is a well suited approach to detect hidden patterns in malware data and thus to support SIEM.
分析恶意软件日志数据以支持安全信息和事件管理:一些研究成果
企业信息基础设施通常以支持决策者履行其职责的大量信息系统为特征。信息安全管理的目标是保护这些系统,而安全信息和事件管理(SIEM)解决的是那些关注事件的短期处理以及整个信息安全体系结构的长期改进的信息管理任务。这是基于那些可以在企业信息安全基础设施中记录和收集的数据来执行的。一种特别有趣的日志数据类型是反恶意软件创建的数据。本文在一个项目案例研究的背景下演示了数据挖掘(DM)是一种非常适合检测恶意软件数据中隐藏模式的方法,从而支持SIEM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信