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Adversarial genetic programming for cyber security: a rising application domain where GP matters

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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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Notes

  1. Computational cost is shown for two populations.

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Acknowledgements

This was supported by the CSAIL CyberSecurity Initiative. This material is based upon work supported by DARPA. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements. Either expressed or implied of Applied Communication Services, or the US Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 799078.

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Correspondence to Jamal Toutouh.

Appendix A

Appendix A

1.1 A.1 Firefly squid bioluminescence defense

When firefly squid are seen from below by a predator, the bioluminescence helps to match the squid’s brightness and color to the sea surface above.

1.2 A.2 A coevolutionary perspective of the U.S. automotive industry

Talay, Calantone, and Voorhees presented an study that explicitly terms competitive interactions between firms “Red Queen competition”, in which gains from innovations are relative and impermanent [133].

1.3 A.3 Advanced persistent threats and ransomware

Some DOS attacks includes Advanced Persistent Threats (APT) and Ransomware. The first ones have multiple stages starting at external reconnaissance then moving to intrusion (e.g. social engineering or use of zero day exploits), laterally moving malware, command and control direction to data exfiltration and, finally, self-erasure. Ransomware, which largely preys upon unpatched systems and which exploits anonymous payment channels enabled by Bitcoin, has also recently become more frequent.

1.4 A.4 Cyber security attack categorization

Examples of attacks, classifications and taxonomies can be found at https://cwe.mitre.org/index.html. One categorization is: (A) Advanced Persistent Threats, “lurking” threats from resourceful persevering adversaries. (B) Denial of Service Attack, defense resource limitation and exposure, means of penetrating systems. (C) Identity theft, e.g. impersonating users. Attacks are also characterized by their stages on a timeline. Another characterization is based on the attacker identity, from individuals to organized criminals and nation states, and what resources they access, see [21] for details.

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O’Reilly, UM., Toutouh, J., Pertierra, M. et al. Adversarial genetic programming for cyber security: a rising application domain where GP matters. Genet Program Evolvable Mach 21, 219–250 (2020). https://doi.org/10.1007/s10710-020-09389-y

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