Scalability, Fidelity and Containment in the Potemkin Virtual Honeyfarm

Michael Vrable, Justin Ma, Alex C. Snoeren, Geoffrey M. Voelker, Stefan Savage
Department of Computer Science and Engineering
University of California, San Diego

Jay Chen
Dept. of Computer Science @ Courant Institute of Mathematical Sciences
New York University
New York, NY 10012

David Moore
Cooperative Association for Internet Data Analysis - CAIDA
San Diego Supercomputer Center,
University of California, San Diego

Erik Vandekieft
IBM, North Carolina

The rapid evolution of large-scale worms, viruses and botnets have made Internet malware a pressing concern. Such infections are at the root of modern scourges including DDoS extortion, on-line identity theft, SPAM, phishing, and piracy. However, the most widely used tools for gathering intelligence on new malware - network honeypots - have forced investigators to choose between monitoring activity at a large scale or capturing behavior with high fidelity. In this paper, we describe an approach to minimize this tension and improve honeypot scalability by up to six orders of magnitude while still closely emulating the execution behavior of individual Internet hosts. We have built a prototype honeyfarm system, called Potemkin, that exploits virtual machines, aggressive memory sharing, and late binding of resources to achieve this goal. While still an immature implementation, Potemkin has emulated over 64,000 Internet honeypots in live test runs, using only a handful of physical servers.