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.

中文翻译：

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用于网络安全的对抗性基因编程：GP 很重要的新兴应用领域

网络安全对手和参与无处不在且不断。我们描述了网络安全的对抗性遗传编程，这是一个研究课题，通过遗传编程 (GP)，复制和研究网络对手的行为及其参与的动态。用于网络安全的对抗性遗传编程包括在一个重要问题领域中现存和直接的研究工作，可以说在 GP 重要的前沿占据一席之地。此外，它通过用 GP 表达不同的抽象以及重新连接到机器学习、人工生命、基于代理的建模和网络安全社区的机会，提出了关于不断发展的复杂行为的研究问题。我们提出了一个名为 RIVALS 的框架，它支持网络安全军备竞赛的研究。