  Artificial intelligence methods have often been applied to perform specific
functions or tasks in the cyber-defense realm. However, as adversary methods
become more complex and difficult to divine, piecemeal efforts to understand
cyber-attacks, and malware-based attacks in particular, are not providing
sufficient means for malware analysts to understand the past, present and
future characteristics of malware.
  In this paper, we present the Malware Analysis and Attributed using Genetic
Information (MAAGI) system. The underlying idea behind the MAAGI system is that
there are strong similarities between malware behavior and biological organism
behavior, and applying biologically inspired methods to corpora of malware can
help analysts better understand the ecosystem of malware attacks. Due to the
sophistication of the malware and the analysis, the MAAGI system relies heavily
on artificial intelligence techniques to provide this capability. It has
already yielded promising results over its development life, and will hopefully
inspire more integration between the artificial intelligence and cyber--defense
communities.
