Ransomware detection using machine learning algorithms

• Published in: Concurrency and computation 2020-09, Vol.32 (18), p.n/a
• Main Authors: Bae, Seong Il, Lee, Gyu Bin, Im, Eul Gyu
• Format: Article
• Language: English
• Subjects: Algorithms; Machine learning; Malware; malware analysis; malware detection; network security; Ransomware; ransomware detection
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5422

Summary The number of ransomware variants has increased rapidly every year, and ransomware needs to be distinguished from the other types of malware to protect users' machines from ransomware‐based attacks. Ransomware is similar to other types of malware in some aspects, but other characteristics are clearly different. For example, ransomware generally conducts a large number of file‐related operations in a short period of time to lock or to encrypt files of a victim's machine. The signature‐based malware detection methods, which have difficulties to detect zero‐day ransomware, are not suitable to protect users' files against the attacks caused by risky unknown ransomware. Therefore, a new protection mechanism specialized for ransomware is needed, and the mechanism should focus on ransomware‐specific operations to distinguish ransomware from other types of malware as well as benign files. This paper proposes a ransomware detection method that can distinguish between ransomware and benign files as well as between ransomware and malware. The experimental results show that our proposed method can detect ransomware among malware and benign files.