Ransomware is a type of malware that affects the victim data by modifying, deleting, or blocking their access. In recent years, ransomware attacks have resulted in critical data and financial losses to individuals and industries. These disruptions force the need for developing effective anti-ransomware methods in the research community. However, most of the existing techniques are designed to detect a specific ransomware variant instead of providing a generic solution mainly because of the obfuscation techniques used by ransomware or the use of static analysis methods. In this context, this paper proposes a novel ransomware-detection technique that identifies ransomware attacks by evaluating the current state of a computer system with knowledge of a ransomware attack. The finite-state machine model is used to synthesise the knowledge of the ransomware attack with respect to the victim machine. The proposed method monitors the changes happening in the computer system in terms of utilisation, persistence, and lateral movement of its resources to detect ransomware attacks. The experimental results demonstrate that the proposed method can accurately detect attacks from different ransomware variants with significantly few false predictions.
Dr. Gowtham R. and Menen, A., “Automated dynamic approach for detecting ransomware using finite-state machine”, Decision Support Systems, vol. 138, p. 113400, 2020.