Abstract
Ransomware is a widespread class of malware that encrypts files in a victim’s computer and extorts victims into paying a fee to regain access to their data. Previous research has proposed methods for ransomware detection using machine learning techniques. However, this research has not examined the precision of ransomware detection. While existing techniques show an overall high accuracy in detecting novel ransomware samples, previous research does not investigate the discrimination of novel ransomware from benign cryptographic programs. This is a critical, practical limitation of current research; machine learning based techniques would be limited in their practical benefit if they generated too many false positives (at best) or deleted/quarantined critical data (at worst). We examine the ability of machine learning techniques based on Application Programming Interface (API) profile features to discriminate novel ransomware from benign-cryptographic programs. This research provides a ransomware detection technique that provides improved detection accuracy and precision compared to other API profile based ransomware detection techniques while using significantly simpler features than previous dynamic ransomware detection research.
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Acknowledgement
This research was funded in part through the Internet Commerce Security Laboratory (ICSL), a joint venture between Westpac, IBM, and Federation University Australia. Paul Black is supported by an Australian Government Research Training Program (RTP) Fee-Offset Scholarship through Federation University Australia. This research was partially supported by funding from the Oceania Cyber Security Centre (OCSC).
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Black, P., Sohail, A., Gondal, I., Kamruzzaman, J., Vamplew, P., Watters, P. (2020). API Based Discrimination of Ransomware and Benign Cryptographic Programs. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_15
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