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Nowadays, with the developing technology, the amount of data that is owned and
processed is increasing day by day. It is very important to ensure the security of data, which is one
of the biggest assets for institutions and organizations. With traditional security methods, attacks
can be detected and prevented, but cybercriminals spend a lot of time and resources on advanced
and targeted attacks that can bypass these methods. The present methods are reactive because they
are generally updated with the information obtained from the analyzes performed after a successful
attack. More proactive approaches are needed to improve safety. Cyber threat intelligence
represents such a proactive approach and involves collecting and analyzing information for
potential threats from a wide variety of data sources. The purpose of cyber-threat intelligence is to
proactively adapt security controls to understand the methodology used by different attackers and
to detect and prevent such activities. In the world of technology, the defense against attacks is one
of the most important issues. Today, different approaches and effective methods have been used
to obtain intelligence. These include vital information about security threats, which are used by
hacker forums and other platforms as a means of communication between hackers. The amount of
data on such platforms is very large. The manual analysis of these data is time-consuming,
ineffective and requires a considerable amount of resources. In this sense, machine learning has
become one of the popular approaches used in the field of cyber-threat intelligence in terms of its
suitability to the subject, producing beneficial and effective results. In this study, information is
given about cyber threat intelligence and in the world of hackers, how to obtain intelligence by
using machine learning techniques is examined and evaluated in detail by supporting the studies
conducted in the literature.
International Data Science & Engineering Symposium
IDSES
Cemile SARICAOĞLU
Mehmet Demirci