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b'Automated identification of malicious code and subsequent classification into known malware families can help cut down laborious manual malware analysis time. Call sequence, assembly instruction statistics and graph topology all say something about the code. This talk will present three identification and classification approaches that use methods and results from complex network theory. Some familiarity with assembly, Win32 architecture, statistics and basic graph theory is helpful.\\n\\t\\n\\tDaniel Bilar is an academic researcher who enjoys poking his nose in code and networks and trying novel ways to solve problems. He has degrees from Brown University (BA, Computer Science), Cornell University (MEng, Operations Research and Industrial Engineering) and Dartmouth College (PhD, Engineering Sciences). Dartmouth College filed a provisional patent for his PhD thesis work ("Quantitative Risk Analysis of Computer Networks", Prof. G. Cybenko advisor), which addresses the problem of risk opacity of software on wired and wireless computer networks.\\n\\t\\n\\tDaniel is a founding member of the Institute for Security and Technology Studies at Dartmouth College. ISTS conducts counter-terrorism technology research, development, and assessment for the Department of Homeland Security. He was part of the group that researches new methods of protecting the nation\'s communication infrastructure. He also was a SANS GIAC Systems and Network Auditor Advisory Board member 2002-2005. Daniel is currently the Hess Fellow in Computer Science at Wellesley College (MA). He has previously developed and taught computer science undergraduate courses on network/computer security, and complex network theory at Oberlin College (OH) and Colby College (ME)."'