Ransomware threat mitigation through network traffic analysis and machine learning techniques
This addresses the threat of ransomware attacks for organizations and users, but it appears incremental as it applies existing machine learning techniques to network traffic analysis.
The paper tackled the problem of ransomware detection in computer networks by using machine learning algorithms to analyze network traffic patterns, achieving high precision and accuracy in identifying ransomware.
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.