JavaScript-based malware attacks have increased in recent years and currently represent a signicant threat to the use of desktop computers, smartphones, and tablets. While static and runtime methods for malware detection have been proposed in the literature, both on the client side, for just-in-time in-browser detection, as well as offline, crawler-based malware discovery, these approaches encounter the same fundamental limitation. Web-based malware tends to be environment-specific, targeting a particular browser, often attacking specic versions of installed plugins. This targeting occurs because the malware exploits vulnerabilities in specific plugins and fails otherwise. As a result, a fundamental limitation for detecting a piece of malware is that malware is triggered infrequently, only showing itself when the right environment is present. We observe that, using fingerprinting techniques that capture and exploit unique properties of browser configurations, almost all existing malware can be made virtually impssible for malware scanners to detect. This paper proposes Rozzle, a JavaScript multi-execution virtual machine, as a way to explore multiple execution paths within a single execution so that environment-specific malware will reveal itself. Using large-scale experiments, we show that Rozzle increases the detection rate for offline runtime detection by almost seven times. In addition, Rozzle triples the effectiveness of online runtime detection. We show that Rozzle incurs virtually no runtime overhead and allows us to replace multiple VMs running different browser configurations with a single Rozzle-enabled browser, reducing the hardware requirements, network bandwidth, and power consumption.

Malware continues to remain one of the most important security problems on the Internet today. Whenever an anti-malware solution becomes popular, malware authors typically react promptly and modify their programs to evade defense mechanisms. For example, recently, malware authors have increasingly started to create malicious code that can evade dynamic analysis.

To handle the growing flood of malware, security vendors and analysts rely on tools that automatically identify and analyze malicious code. Current systems for automated malware analysis typically follow a dynamic approach, executing an unknown program in a controlled environment (sandbox) and recording its runtime behavior. Since dynamic analysis platforms directly run malicious code, they are resilient to popular malware defense techniques such as packing and code obfuscation. Unfortunately, in many cases, only a small subset of all possible malicious behaviors is observed within the short time frame that a malware sample is executed. To mitigate this issue, previous work introduced techniques such as multipath or forced execution to increase the coverage of dynamic malware analysis. Unfortunately, using these techniques is potentially expensive, as the number of paths that require analysis can grow exponentially. In this paper, we propose REANIMATOR, a novel solution to determine the capabilities (malicious functionality) of malware programs. Our solution is based on the insight that we can leverage behavior observed while dynamically executing a specific malware sample to identify similar functionality in other programs. More precisely, when we observe malicious actions during dynamic analysis, we automatically extract and model the parts of the malware binary that are responsible for this behavior. We then leverage these models to check whether similar code is present in other samples. This allows us to statically identify dormant functionality (functionality that is not observed during dynamic analysis) in malicious programs. We evaluate our approach on thousands of realworld malware samples, and we show that our system is successful in identifying additional, malicious functionality. As a result, our approach can significantly improve the coverage of malware analysis results.

Unfortunately, malicious software is still an unsolved problem and a major threat on the Internet. An important component in the fight against malicious software is the analysis of malware samples: Only if an analyst understands the behavior of a given sample, she can design appropriate countermeasures. Manual approaches are frequently used to analyze certain key algorithms, such as downloading of encoded updates, or generating new DNS domains for command and control purposes. In this paper, we present a novel approach to automatically extract, from a given binary executable, the algorithm related to a certain activity of the sample. We isolate and extract these instructions and generate a so-called gadget, i.e., a stand-alone component that encapsulates a specific behavior. We make sure that a gadget can autonomously perform a specific task by including all relevant code and data into the gadget such that it can be executed in a self-contained fashion. Gadgets are useful entities in analyzing malicious software: In particular, they are valuable for practitioners, as understanding a certain activity that is embedded in a binary sample (e.g., the update function) is still largely a manual and complex task. Our evaluation with several real-world samples demonstrates that our approach is versatile and useful in practice.