Ransomware threat mitigation through network traffic analysis and machine learning techniques

2024/01/27 by Ali Mehrban, Mehrban, Ali, Shirin Karimi Geransayeh +1
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #Cybercrime and Law Enforcement Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2401.15285

openalex publication_date 2024/01/27 · openalex created_date 2024/02/01 · openalex updated_date 2026/07/28

Abstract

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.

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