  Cyber security adversaries and engagements are ubiquitous and ceaseless. We
delineate Adversarial Genetic Programming for Cyber Security, a research topic
that, by means of genetic programming (GP), replicates and studies the behavior
of cyber adversaries and the dynamics of their engagements. Adversarial Genetic
Programming for Cyber Security encompasses extant and immediate research
efforts in a vital problem domain, arguably occupying a position at the
frontier where GP matters. Additionally, it prompts research questions around
evolving complex behavior by expressing different abstractions with GP and
opportunities to reconnect to the Machine Learning, Artificial Life,
Agent-Based Modeling and Cyber Security communities. We present a framework
called RIVALS which supports the study of network security arms races. Its goal
is to elucidate the dynamics of cyber networks under attack by computationally
modeling and simulating them.
