Adversarial genetic programming for cyber security: a rising application domain where GP matters
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10710_2020_9389_ReferencePDF.pdf
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2.82 MB
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Author(s)
O’Reilly, Una-May
Toutouh, Jamal
Pertierra, Marcos
Sanchez, Daniel P
Garcia, Dennis
Luogo, Anthony E
Kelly, Jonathan
Hemberg, Erik
Date Issued
April 2, 2020
Publisher
Springer US
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10710-020-09389-y
