Due to a lack of space and time security experts tend to reference security webapges in their posts on social media platforms. As these references include valuable information regarding vulnerbalities, threats, or countermeasures they are of potential interest for the whole cyber security community. According to the large number of posts referencing security webpages on social media platforms it is barely feasible to check their contents manually. Therefore, a tool to automaticall classify and cluster security webpages will be needed. The goal of this thesis is to analyze the state of the art of webpage classification techniques and implement a tool to classify and cluster cyber security webpages.
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