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Big Data for Cybersecurity: Vulnerability Disclosure Trends and Dependencies

Abstract:
Complex Big Data systems in modern organisations are progressively becoming attack targets by existing and emerging threat agents. Elaborate and specialised attacks will increasingly be crafted to exploit vulnerabilities and weaknesses. With the ever-increasing trend of cybercrime and incidents due to these vulnerabilities, effective vulnerability management is imperative for modern organisations regardless of their size. However, organisations struggle to manage the sheer volume of vulnerabilities discovered on their networks. Moreover, vulnerability management tends to be more reactive in practice. Rigorous statistical models, simulating anticipated volume and dependence of vulnerability disclosures, will undoubtedly provide important insights to organisations and help them become more proactive in the management of cyber risks. By leveraging the rich yet complex historical vulnerability data, our proposed novel and rigorous framework has enabled this new capability. By utilising this sound framework, we initiated an important study on not only handling persistent volatilities in the data but also further unveiling multivariate dependence structure amongst different vulnerability risks. In sharp contrast to the existing studies on univariate time series, we consider the more general multivariate case striving to capture their intriguing relationships. Through our extensive empirical studies using the real world vulnerability data, we have shown that a composite model can effectively capture and preserve long-term dependency between different vulnerability and exploit disclosures. In addition, the paper paves the way for further study on the stochastic perspective of vulnerability proliferation towards building more accurate measures for better cyber risk management as a whole.
Author Listing: MingJian Tang;Mamoun Alazab;Yuxiu Luo
Volume: 5
Pages: 317-329
DOI: 10.1109/TBDATA.2017.2723570
Language: English
Journal: IEEE Transactions on Big Data

IEEE Transactions on Big Data

IEEE T BIG DATA

影响因子:5.7
是否综述期刊:否
是否OA:否
是否预警:不在预警名单内
发行时间:-
ISSN:2332-7790
发刊频率:-
收录数据库:SCIE/Scopus收录
出版国家/地区:UNITED STATES
出版社:IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

期刊介绍

年发文量 83
国人发稿量 68
国人发文占比 81.36%
自引率 3.5%
平均录取率 -
平均审稿周期 -
版面费 US$2195
偏重研究方向 Multiple-
期刊官网 -
投稿链接 -

质量指标占比

研究类文章占比 OA被引用占比 撤稿占比 出版后修正文章占比
98.80% 3.57% 0.00% 0.00%

相关指数

影响因子
影响因子
年发文量
自引率
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预警情况

时间 预警情况
2025年03月发布的2025版 不在预警名单中
2024年02月发布的2024版 不在预警名单中
2023年01月发布的2023版 不在预警名单中
2021年12月发布的2021版 不在预警名单中
2020年12月发布的2020版 不在预警名单中

JCR分区 WOS分区等级:Q1区

版本 按学科 分区
WOS期刊SCI分区
(2021-2022年最新版)
COMPUTER SCIENCE, THEORY & METHODS Q1
COMPUTER SCIENCE, INFORMATION SYSTEMS Q1

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COMPUTER SCIENCE, THEORY & METHODS
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2021年12月
基础版
工程技术
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计算机:信息系统
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COMPUTER SCIENCE, THEORY & METHODS
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2021年12月
升级版
计算机科学
2区
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计算机:信息系统
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COMPUTER SCIENCE, THEORY & METHODS
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2022年12月
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计算机科学
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COMPUTER SCIENCE, INFORMATION SYSTEMS
计算机:信息系统
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COMPUTER SCIENCE, THEORY & METHODS
计算机:理论方法
2区