The explosion of ransomware in recent years has served as a costly re-minder that the malware threatscape has moved from that of socially-inept hobbyists to career criminals. This paper investigates the efficacy of dynamic opcode analysis in distinguishing cryptographic ransom-ware from benignware, and presents several novel contributions. Firstly, a new dataset of cryptoransomware dynamic run-traces, the largest of its kind in the literature. We release this to the wider research communi-ty to foster further research in the field. Our second novel contribution demonstrates that a short run- length of 32k opcodes can provide highly accurate detection of ransomware (99.56%) compared to benign soft-ware. Third, our model offers a distinct advantage over other models in the literature, in that it can detect a form of benign encryption (i.e. file zipping) with 100% accuracy against not only ransomware, but also the non-encrypting benignware in our dataset. The research presented here demonstrates that dynamic opcode tracing is capable of detecting ransomware in comparable times to static analysis, without being thwarted by obfuscation tactics.
|Number of pages||14|
|Journal||International Journal on Cyber Situational Awareness|
|Publication status||Published - 13 Dec 2018|
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications