Virtual honeypots: from botnet tracking to intrusion detection
Virtual honeypots: from botnet tracking to intrusion detection
SS'08 Proceedings of the 17th conference on Security symposium
Early detection of malicious behavior in JavaScript code
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Autonomous learning for detection of JavaScript attacks: vision or reality?
Proceedings of the 5th ACM workshop on Security and artificial intelligence
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Client-based attacks on internet users with malicious web pages represent a serious and rising threat. Internet Browsers with enabled active content technologies such as JavaScript are vulnerable to so-called drive-by downloads. Drive-by downloads are able to automatically infect a victim's system during a single visit of a crafted web page testing various vulnerabilities and installing e.g. malware files or illegal content without user interaction. In this paper we present MonkeyWrench, a low-interaction web-honeyclient allowing automatic identification of malicious web pages by performing static analysis of the HTML-objects in a web page as well as dynamic analysis of scripts by execution in an emulated browser environment. Using this hybrid approach MonkeyWrench overcomes shortcomings of existing low-interaction web-honeyclients in dealing with obfuscated JavaScript while outperforming high-interaction systems. Further MonkeyWrench is able to identify the exact vulnerability triggered by a malicious page and to extract payloads from within obfuscated scripts which are valuable information to security analysts and researchers. Results of an examination of several hundred thousand web pages demonstrate MonkeyWrench's ability to expose rising threats of the web, and to collect malware and JavaScript exploit samples.