Ransomware Detection Using Machine Learning: A Review, Research Limitations and Future Directions

Abstract

Ransomware attacks are on the rise in terms of both frequency and impact. The shift to remote work due to the COVID-19 pandemic has led more people to work online, prompting companies to adapt quickly. Unfortunately, this increased online activity has provided cybercriminals numerous opportunities to carry out devastating attacks. One recent method employed by malicious actors involves infecting corporate networks with ransomware to extract millions of dollars in profits. Ransomware falls into the category of malware. It works by encrypting sensitive data and demanding payments from victims to receive the encryption keys necessary for decrypting their data. The prevalence of this type of attack has prompted governments and organisations worldwide to intensify their efforts to combat ransomware. In response, the research community has also focused on ransomware detection, leveraging technologies such as machine learning. Despite this increased attention, practical solutions for real-world applications remain scarce in the existing literature. Numerous surveys have explored literature within the domain. Still, there is a notable lack of emphasis on the design of ransomware detection systems and the practical aspects of detection, including real-time and early detection. Against this backdrop, our review delves into the existing literature on ransomware detection, specifically examining the machine-learning techniques, detection approaches, and designs employed. Finally, we highlight the limitations of prior studies and propose future research directions in this crucial area.

Keywords

• Ransomware detection
• machine learning
• deep learning
• early detection
• real-time detection
• survey