Ransomware Classification: A Comparative Analysis of ML Algorithms

The increasing prevalence of ransomware, malware, and malicious cyberattacks poses a significant threat to computer networks, data centers, websites, mobile applications, and industrial environments across various business sectors. Ransomware, a type of malicious software, encrypts victims’ files and demands a ransom for the decryption key, creating widespread disruption for individuals and organizations globally. As ransomware continues to evolve and grow in sophistication, the timely and accurate detection of ransomware has become essential. This study aims to address this critical issue by providing a comprehensive analysis of ransomware variants and exploring the use of machine learning techniques to enhance detection capabilities. The research employs a systematic review of existing ransomware detection methods and evaluates the potential of machine learning models to classify and detect different ransomware variant groups. The findings highlight the strengths and limitations of various approaches and demonstrate the effectiveness of machine learning in identifying ransomware patterns. This study’s insights are expected to contribute to the development of more robust and adaptive defense mechanisms against the ever-evolving threat of ransomware.