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Cyber-Attack Features for Detecting Cyber Threat Incidents from Online News
Mohamad Syahir Abdullah & Anazida Zainal
Abstract
There are large volume of data from the online news sources that are freely available
which might contain valuable information. Data such as cyber-attacks news keep
growing bigger and can be analyzed to gather informative insights of current situation.
However, news is reported in many styles, added with the emerging of new cyber-attack
and the ambiguous terms used have made the detection of the related news become
more difficult. Thus, to handle these situations, the aim of this paper is to propose a
scheme on detecting the related news about cyber-attacks. The scheme starts with
identifying the cyber-attack features which will be used to classify the cyber-attack
news. The scheme also includes a machine learning approach using Conditional
Random Field (CRF) classifier and Latent Semantic Analysis (LSA) for further analysis.
The results from this research should help people by showing the actual picture of
cyber-attack occurrences in our surrounding and give valuable information to public thus
raising social awareness about cyber-attack activities.
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