Digital Currency Features Oriented Fine-Grained Code Injection Attack Detection

Digital currencies have developed rapidly and emerged as a critical form of our payment system. Consequently, the applications and platforms of digital currencies and their payment services are extensively exposed to various exploits by malware. In a typical scenario, modern ransomware usually leverages digital currencies as the medium of payment. The state-of-the-art code injection attack detections have rarely considered such digital currency-related memory features, thus can hardly identify the malicious behaviors of ransomware. To mitigate this issue, we propose a fine-grained scheme of memory forensics to facilitate the detection of host-based code injection attacks with the ability to identify ransomware. We capture the digital currency-related memory features exhibited in the procedure of inducing the victims' payment. We incorporate such memory features into a set of general memory features and implement a fine-grained detection system on code injection attacks. According to the experimental results, the new scheme of memory forensics effectively improves the performance of the state-of-the-art detection system on different metrics. Meanwhile, our approach enables the detection systems of host-based code injection attacks to capture the behaviors of ransomware precisely. Moreover, the extraction of the newly proposed memory features is efficient, and our detection system is capable of detecting unknown malware families. © 2021, Science Press. All right reserved.