Problem
Abstract—JavaScript-based malware attacks have in- creased in recent years and currently represent a significant 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 specific versions of installed plugins.
Approach
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 environ- ment 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 impossible for malware scanners to detect.
Results
This paper proposes Rozzle, a JavaScript multi- execution virtual machine, as a way to explore multi- ple 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.