Conference paper Open Access

Towards Selecting Informative Content for Cyber Threat Intelligence

Panos Panagiotou; Christos Iliou; Konstantinos Apostolou; Theodora Tsikrika; Stefanos Vrochidis; Periklis Chatzimisios; Ioannis Kompatsiaris

Nowadays, there is an increasing need for cyber security professionals to make use of tools that automatically extract Cyber Threat Intelligence (CTI) relying on information collected from relevant blogs and news sources that are publicly available. When such sources are used, an important part of the CTI extraction process is content selection, in which pages that do not contain CTI-related information should be filtered out. For this task, we apply supervised machine learning-based text classification techniques, trained on a new dataset created for the purposes of this work. Furthermore, we show in practice the importance of a good content selection process in a commonly used CTI extraction pipeline, by inspecting the results of the Named Entity Recognition (NER) process that normally follows.

This is the accepted version of the paper. The final version of the paper can be found at https://ieeexplore.ieee.org/abstract/document/9527909
Files (155.6 kB)
Name Size
2021_IEEE_CSR_ACTI_Towards_Selecting_Informative_Content_for_CTI.pdf
md5:ee1226fb4045ff2193cba9a27d3b87b0
155.6 kB Download
26
40
views
downloads
Views 26
Downloads 40
Data volume 6.2 MB
Unique views 22
Unique downloads 34

Share

Cite as