Rozzle: De-Cloaking Internet Malware

Clemens Kolbitsch, Benjamin Livshits, Benjamin Zorn, and Christian Seifert

Abstract

In recent years, attacks that exploit vulnerabilities in browsers and their associated plugins have increased significantly. These attacks are often written in JavaScript and literally millions of URLs contain such malicious content.

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. This targeting occurs because the malware exploits vulnerabilities in specific plugins and fail 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. In fact, we observe that using current fingerprinting techniques, just about any piece of existing malware may be made virtually undetectable with the current generation of malware scanners.

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.

Details

Publication typeTechReport
NumberMSR-TR-2011-94

Previous versions

Paruj Ratanaworabhan, Benjamin Livshits, and Benjamin G. Zorn. Nozzle: A Defense Against Heap-spraying Code Injection Attacks, November 2008.

Paruj Ratanaworabhan, Benjamin Livshits, and Benjamin Zorn. Nozzle: A Defense Against Heap-spraying Code Injection Attacks, USENIX, 2009.

Charles Curtsinger, Benjamin Livshits, Benjamin Zorn, and Christian Seifert. Zozzle: Low-overhead Mostly Static JavaScript Malware Detection, 27 November 2010.

Charles Curtsinger, Benjamin Livshits, Benjamin Zorn, and Christian Seifert. Zozzle: Low-overhead Mostly Static JavaScript Malware Detection, 27 November 2010.

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