  In recent years, there has been a noticeable increase in cyberattacks using
ransomware. Attackers use this malicious software to break into networks and
harm computer systems. This has caused significant and lasting damage to
various organizations, including government, private companies, and regular
users. These attacks often lead to the loss or exposure of sensitive
information, disruptions in normal operations, and persistent vulnerabilities.
This paper focuses on a method for recognizing and identifying ransomware in
computer networks. The approach relies on using machine learning algorithms and
analyzing the patterns of network traffic. By collecting and studying this
traffic, and then applying machine learning models, we can accurately identify
and detect ransomware. The results of implementing this method show that
machine learning algorithms can effectively pinpoint ransomware based on
network traffic, achieving high levels of precision and accuracy.
