The World of Defacers: Looking Through the Lens of Their Activities on Twitter
The World of Defacers: Looking Through the Lens of Their Activities on Twitter
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DOI：
10.1109/access.2020.3037015
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Tian, Hao
中科院分区：
文献类型：
--
作者：
Aslan, Cagri Burak;Li, Shujun;Tian, Hao
许多基于Web的攻击已被研究，以了解网络黑客的行为，但网站污损攻击（受害者网站的恶意内容操纵）和污损者的行为受到研究人员的关注较少。本文填补了这一研究空白，通过一个公共数据库的污损和污损攻击和活动的96个选定的污损谁是活跃在Twitter上的计算数据驱动的分析。我们对数据进行了全面的分析：分析了一个有10,360个节点的友谊图，分析了破坏者的情绪与攻击模式的关系，并进行了基于主题建模的分析，以研究破坏者在Twitter上公开讨论了什么。我们的分析揭示了一些关键发现：模块化和层次聚类方法可以帮助发现有趣的污损者子社区;情感分析可以帮助根据攻击模式对污损者的行为进行分类;主题建模揭示了Twitter上污损者之间的一些焦点主题（政治，国家特定主题和技术讨论）以及分享类似主题的污损者的地理链接。我们相信这些发现有助于更好地了解污损者的行为，这可以帮助设计和开发更好的解决方案来检测污损者，甚至防止阻碍污损攻击。
Many web-based attacks have been studied to understand how web hackers behave, but web site defacement attacks (malicious content manipulations of victim web sites) and defacers' behaviors have received less attention from researchers. This paper fills this research gap via a computational data-driven analysis of a public database of defacers and defacement attacks and activities of 96 selected defacers who were active on Twitter. We conducted a comprehensive analysis of the data: an analysis of a friendship graph with 10,360 nodes, an analysis on how sentiments of defacers related to attack patterns, and a topical modelling based analysis to study what defacers discussed publicly on Twitter. Our analysis revealed a number of key findings: a modular and hierarchical clustering method can help discover interesting sub-communities of defacers; sentiment analysis can help categorize behaviors of defacers in terms of attack patterns; and topic modelling revealed some focus topics (politics, country-specific topics, and technical discussions) among defacers on Twitter and also geographic links of defacers sharing similar topics. We believe that these findings are useful for a better understanding of defacers' behaviors, which could help design and development of better solutions for detecting defacers and even preventing impeding defacement attacks.