Cybersecurity researchers have contributed to the automated extraction of CTI
from textual sources, such as threat reports and online articles, where
cyberattack strategies, procedures, and tools are described. The goal of this
article is to aid cybersecurity researchers understand the current techniques
used for cyberthreat intelligence extraction from text through a survey of
relevant studies in the literature. We systematically collect “CTI extraction
from text”-related studies from the literature and categorize the CTI
extraction purposes. We propose a CTI extraction pipeline abstracted from these
studies. We identify the data sources, techniques, and CTI sharing formats
utilized in the context of the proposed pipeline. Our work finds ten types of
extraction purposes, such as extraction indicators of compromise extraction,
TTPs (tactics, techniques, procedures of attack), and cybersecurity keywords.
We also identify seven types of textual sources for CTI extraction, and textual
data obtained from hacker forums, threat reports, social media posts, and
online news articles have been used by almost 90% of the studies. Natural
language processing along with both supervised and unsupervised machine
learning techniques such as named entity recognition, topic modelling,
dependency parsing, supervised classification, and clustering are used for CTI
extraction. We observe the technical challenges associated with these studies
related to obtaining available clean, labelled data which could assure
replication, validation, and further extension of the studies. As we find the
studies focusing on CTI information extraction from text, we advocate for
building upon the current CTI extraction work to help cybersecurity
practitioners with proactive decision making such as threat prioritization,
automated threat modelling to utilize knowledge from past cybersecurity
incidents.

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