Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters

• URL: http://arxiv.org/abs/2004.04647v1
• Date: Tue, 7 Apr 2020 00:13:14 GMT
• Title: Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters
• Authors: Una-May O'Reilly and Jamal Toutouh and Marcos Pertierra and Daniel Prado Sanchez and Dennis Garcia and Anthony Erb Luogo and Jonathan Kelly and Erik Hemberg
• Abstract summary: Adversarial Genetic Programming for Cyber Security is a research topic that studies the behavior of cyber adversaries and the dynamics of their engagements. It prompts research questions around evolving complex behavior by expressing different abstractions with GP. We present a framework called RIVALS which supports the study of network security arms races.
• Score: 10.336476147660383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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