Fostering Cybersecurity Big Data Research : A Case Study of the AZSecure Data System
Fostering Cybersecurity Big Data Research : A Case Study of the AZSecure Data System
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促进网络安全大数据研究：AZSecure 数据系统案例研究
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Hsinchun Chen
中科院分区：
文献类型：
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作者：
Resha Shenandoah;Sagar Samtani;Mark W. Patton;Hsinchun Chen
目前的估计是，网络犯罪每年造成的全球经济损失为4500亿美元。自2003年以来，国际情报和安全信息学(ISI)社区(1,500多名成员)已被公认为通过计算收集和分析网络威胁情报、恐怖主义信息学和大数据网络安全等高影响和新兴主题的安全数据集方面的领先专家。尽管三军情报局取得了许多成功，但目前还没有三军情报局数据集和分析工具的存储库。本文介绍了AZSecure数据系统，该系统致力于促进网络安全中的研究数据共享和重现性。AZSecure Data是一个由美国国家科学基金会(NSF)数据基础设施构建块(DIBBS)计划慷慨资助的多年、多机构项目。AZSecure数据系统围绕图书馆学的访问和发现原则设计，拥有七种数据类型的22个集合：论坛和聊天日志、推文、恶意软件、网络流量钓鱼、报纸和其他网站。AZSecure数据已经在国际上得到了使用，有几个数据集已经用于学术出版。关键词-网络安全、数据仓库、网络安全大数据分析、网络威胁情报、数据共享
Current estimates place the global economic cost of cybercrime at $450 billion annually. Since 2003, the international Intelligence and Security Informatics (ISI) community (1,500+ members) has been recognized as leading experts in computationally collection and analysis of security datasets for high-impact and emerging topics such as Cyber Threat Intelligence, terrorism informatics, and Big Data cybersecurity. Despite ISI’s numerous successes, no repository of ISI datasets and analytic tools currently exists. This paper presents the AZSecure Data system, an effort which strives to promote research data sharing and reproducibility in cybersecurity. AZSecure Data is a multi-year, multi-institution project generously funded by the National Science Foundation (NSF) Data Infrastructure Building Blocks (DIBBs) program. Designed around library science principles of access and discovery, the AZSecure Data system has 22 collections across seven data types: forums and chat logs, tweets, malware, network traffic phishing, newspapers, and other websites. AZSecure Data has seen international usage, with several datasets already used for scholarly publication. Keywords— cybersecurity, data repository, cybersecurity big data analytics, cyber threat intelligence, data sharing