Detecting Malicious Campaigns in Obfuscated JavaScript with Scalable Behavioral Analysis

Modern security crawlers and firewall solutions have to analyze millions of websites on a daily basis, and significantly more JavaScript samples. At the same time, fast static approaches, such as file signatures and hash matching, often are not enough to detect advanced malicious campaigns, i.e., obfuscated, packed, or randomized scripts. As such, low-overhead yet efficient dynamic analysis is required. In the current paper we describe behavioral analysis after executing all the scripts on web pages, similarly to how real browsers do. Then, we apply light "behavioral signatures" to the collected dynamic indicators, such as global variables declared during runtime, popup messages shown to the user, established WebSocket connections. Using this scalable method for a month, we enhanced the coverage of a commercial URL filtering product by detecting 8,712 URLs with intrusive coin miners. We evaluated the impact of increased coverage through telemetry data and discovered that customers attempted to visit these abusive sites more than a million times. Moreover, we captured 4,633 additional distinct URLs that lead to scam, clickjacking, phishing, and other kinds of malicious JavaScript. Our findings provide insight into recent trends in unauthorized cryptographic coin-mining and show that various scam kits are currently active on the Web.