Longitudinal Analysis of Cyber-Related Articles

2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)(2020)

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摘要
Cyber attacks have a massive impact on a worldwide economy that is ever-growing in its reliance on technology. With the increase in internet connectivity, more individuals, as well as enterprises, are vulnerable to cyber attacks. Also, in recent years, cyber-related news attracted significant attention from news outlets as well as viewers. Currently, there are many news outlets dedicated to covering technology news in general, and cyber news in particular. In this work, we conduct a correlation analysis over four years of cyber-related news articles obtained from the Global Data on Events, Location, and Tone data source. We apply both supervised and unsupervised text analysis techniques to understand spatial, temporal and distributional topic patterns. Experimental results show interesting trends with respect to cyber-attacks such as ransomware, data breach and denial of service attacks as well as more general cyber-related concepts such as cryptocurrency. This work helps practitioners in understanding an increasingly evolving spectrum of cyber events.
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关键词
cyber-related news articles,supervised text analysis techniques,unsupervised text analysis techniques,cyber-attacks,general cyber-related concepts,cyber events,longitudinal analysis,cyber-related articles,cyber attacks,news outlets,technology news,cyber news,correlation analysis
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