Autonomous learning for detection of JavaScript attacks: vision or reality?
Proceedings of the 5th ACM workshop on Security and artificial intelligence
You are what you include: large-scale evaluation of remote javascript inclusions
Proceedings of the 2012 ACM conference on Computer and communications security
FlashDetect: actionscript 3 malware detection
RAID'12 Proceedings of the 15th international conference on Research in Attacks, Intrusions, and Defenses
Finding malware on a web scale
MMM-ACNS'12 Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security
Shady paths: leveraging surfing crowds to detect malicious web pages
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
mXSS attacks: attacking well-secured web-applications by using innerHTML mutations
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Weaknesses in defenses against web-borne malware
DIMVA'13 Proceedings of the 10th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Revolver: an automated approach to the detection of evasiveweb-based malware
SEC'13 Proceedings of the 22nd USENIX conference on Security
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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.