论文标题
针对网络安全的对抗性遗传编程：GP很重要的应用程序域上升
Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters
论文作者
论文摘要
网络安全对手和参与无处不在，不断。我们为网络安全描述了对抗性遗传编程，这是一个研究主题，通过基因编程（GP），复制和研究网络对手的行为及其参与的动态。针对网络安全的对抗性遗传编程涵盖了重要的问题领域的现存和即时研究工作，可以说是在GP重要的边境占据位置。此外，它通过与GP表达不同的抽象以及重新连接机器学习，人造生活，基于代理的建模和网络安全社区的机会来提示有关发展复杂行为的研究问题。我们提出了一个名为竞争对手的框架，该框架支持网络安全部门种族的研究。它的目标是通过计算建模和模拟它们在攻击下阐明网络网络的动态。
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.
