The threat of ransomware to the software ecosystem has become increasingly alarming in recent years, raising a demand for large-scale and comprehensive ransomware analysis to help develop more effective countermeasures against unknown attacks. In this paper, we first collect a real-world dataset MarauderMap, consisting of 7,796 active ransomware samples, and analyze their behaviors of disrupting data in victim systems. All samples are executed in isolated testbeds to collect all perspectives of six categories of runtime behaviors, such as API calls, I/O accesses, and network traffic. The total logs volume is up to 1.98 TiB. By assessing collected behaviors, we present six critical findings throughout ransomware attacks' data reconnaissance, data tampering, and data exfiltration phases. Based on our findings, we propose three corresponding mitigation strategies to detect ransomware during each phase. Experimental results show that they can enhance the capability of state-of-the-art anti-ransomware tools. We report a preliminary result of a 41%-69% increase in detection rate with no additional false positives, showing that our insights are helpful.

By encrypting the data of infected hosts, cryptographic ransomware has caused billions of dollars in financial losses to a wide range of victims. Many detection techniques have been proposed to counter ransomware threats over the past decade. Their common approach is to monitor I/O behaviors from user space and apply custom heuristics to discriminate ransomware. These techniques implicitly assume that ransomware behaves very differently from benign programs in terms of heuristics. However, when we investigated the behavior of benign and ransomware programs, we found that the boundary between their behaviors was blurred. A ransomware program can still achieve its goal even though it follows the behavior patterns of benign programs. In this paper, we aim to explore the limits of ransomware detection techniques that based on I/O behaviors. To this end, we present Animagus, an imitation-based ransomware attack that imitates behaviors of benign programs to disguise its encryption tasks. It first learns behavior patterns from a benign program, and then spawns and orchestrates child processes to perform encryption tasks behaving the same as the benign program. We evaluate its effectiveness against six state-of-the-art detection techniques, and the results show that it can successfully evade these defenses. We investigate in detail why they are ineffective and how Animagus is different from existing ransomware samples. In the end, we discuss potential countermeasures and the benefits that detection tools can gain from our work.

The number of IoT devices on the Internet has surged recently, accompanied by a barrage of large-scale IoT malware infections breakouts. Designing security mechanisms for IoT devices poses significant challenges due to constantly changing malware variants that have numerous camouflage strategies, limited hardware resources, and heterogeneous architectures. In this article, we propose MIDAS, an adaptive safeguard framework for Linux-based IoT devices to defend against malwares with the real-time behavior auditing mechanism. First, we construct a stable and abstract behavior paradigm through behavioral characteristic extraction of 115 970 malwares. Then, based on the behavior paradigm, MIDAS can: 1) monitor suspicious behaviors of break-in programs in real-time driven by our built-in SELinux policy customized for malware defense; 2) aggregate behaviors of the program’s submodules with homology tracing; and 3) summarize these behaviors into abstract behavior pairs to unveil a possible IoT malware. Using the aforementioned real-time behavior auditing, MIDAS can constrain mutating and camouflaged malwares to protect discrepant IoT devices from being compromised while maintaining low overheads. We thoroughly evaluated the defense capabilities of MIDAS. On the benchmark dataset, MIDAS successfully constrained up to 94.46%, 91.79%, and 88.34% of 115970 malware samples on ARM, MIPS, and MIPSEL architectures, with less than 1.8 MiB of memory consumption and 0.54% CPU usage. Furthermore, we deployed virtual IoT devices worldwide to examine the performance of MIDAS when defending against real-world attacks. Over a duration of 25 days, these devices suffered from 971 951 attacks originating from 71 979 intruding malwares and 48 805 unique IPs distributed in 167 countries. For devices with MIDAS protection, the number of compromised incidents decreases by $343.1\times $ , and the duration of continuous operation is $179.2\times $ greater than devices without MIDAS on average. The evaluation results demonstrate that MIDAS can effectively safeguard IoT devices with minimal resource consumption.