ERW-Radar: An Adaptive Detection System against Evasive Ransomware by Contextual Behavior Detection and Fine-grained Content Analysis

—To evade existing antivirus software and detection systems, ransomware authors tend to obscure behavior differences with benign programs by imitating them or by weakening malicious behaviors during encryption. Existing defense solutions have limited effects on defending against evasive ransomware. Fortunately, through extensive observation, we find I/O behaviors of evasive ransomware exhibit a unique repetitiveness during encryption. This is rarely observed in benign programs. Besides, the χ 2 test and the probability distribution of byte streams can effectively distinguish encrypted files from benignly modified files. Inspired by these, we first propose ERW-Radar, a detection system, to detect evasive ransomware accurately and efficiently. We make three breakthroughs: 1) a contextual Correlation mechanism to detect malicious behaviors; 2) a fine-grained content Analysis mechanism to identify encrypted files; and 3) adaptive mechanisms to achieve a better trade-off between accuracy and efficiency. Experiments show that ERW-Radar detects evasive ransomware with an accuracy of 96.18% while maintaining a FPR of 5.36%. The average overhead of ERW-Radar is 5.09% in CPU utilization and 3.80% in memory utilization.