利用机器学习技术可视化网络威胁情报中的有趣模式

IF 1.2 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sarwat Ejaz, Umara Noor, Zahid Rashid
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引用次数: 2

摘要

在一个先进和动态的网络威胁环境中,组织需要产生更主动的方法来处理他们的网络防御。以前事件的网络威胁数据,即网络威胁情报(CTI),通过帮助安全分析师了解最近的网络威胁及其缓解措施,发挥了重要作用。CTI的数量呈指数级增长,大部分内容是文本化的,难以分析。目前的CTI可视化工具不能提供有效的可视化。为了解决这个问题,对CTI报告进行了探索性数据分析,以挖掘和可视化有趣的网络威胁模式,帮助安全分析师主动减轻漏洞并及时预测网络中的网络威胁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing Interesting Patterns in Cyber Threat Intelligence Using Machine Learning Techniques
Abstract In an advanced and dynamic cyber threat environment, organizations need to yield more proactive methods to handle their cyber defenses. Cyber threat data known as Cyber Threat Intelligence (CTI) of previous incidents plays an important role by helping security analysts understand recent cyber threats and their mitigations. The mass of CTI is exponentially increasing, most of the content is textual which makes it difficult to analyze. The current CTI visualization tools do not provide effective visualizations. To address this issue, an exploratory data analysis of CTI reports is performed to dig-out and visualize interesting patterns of cyber threats which help security analysts to proactively mitigate vulnerabilities and timely predict cyber threats in their networks.
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来源期刊
Cybernetics and Information Technologies
Cybernetics and Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.20
自引率
25.00%
发文量
35
审稿时长
12 weeks
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