Reinforcement Learning in Cyber Wargaming Defense
DOI:
https://doi.org/10.37266/ISER.2023v11i1-2.pp60-66Keywords:
Cyber Wargaming, Reinforcement Learning, OntologyAbstract
In recent decades the necessity for cyber security has grown for both private companies as well as government agencies. This growth is the result of increasing ability for organizations to mount cyber-attacks. As a response, organizations have been developing cyber defense artificial intelligence (AI), which greatly improves cyber-security capabilities. This ne- cessitates not only the development of cyber-attack, defense, and vulnerability frameworks to simulate a realistic environment, but also methods with which to train the AI. Further, the number and variety of networks necessitates a framework with which AI can be quickly and cost-effectively trained. This paper will explore how our team has worked to develop an efficient and comprehensive framework under which a variety of AI can be trained to fulfill the need for cyber resiliency.
References
Huang, H. (2020). A collaborative battle in cybersecurity? threats and opportunities for taiwan. Asia Policy, 15(2), 101 - 106. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&db=poh&AN= 143225698&site=ehost-live&scope=site
Lapan, M. (2018). Deep reinforcement learning hands-on: Apply modern rl methods, with deep q-networks, value iteration, policy gradients, trpo, alphago zero and more. Birmingham: Packt Publishing, Limited.
Mello, J. P. (2022). Software supply chain attacks hit three out of five companies in 2021. CSO Online.
Mitre att&ck enterprise matrix. (n.d.). https://attack.mitre.org/matrices/enterprise/. (Accessed: 2022-09-27)
Mitre d3fend. (n.d.). https://d3fend.mitre.org/. (Accessed: 2022-11-08)
Nguyen, T. T., & Reddi, V. J. (2021). Deep reinforcement learning for cyber security. IEEE Transactions on Neural Networks and Learning Systems.
Pratt, M. K. (2021). Cybersecurity spending trends for 2022: Investing in the future. CSO Online.
Sewak, M., Sahay, S. K., & Rathore, H. (2022). Deep reinforcement learning for cybersecurity threat detection and protection: A review. In Secure knowledge management in the artificial intelligence era: 9th international conference, skm 2021, san antonio, tx, usa, october 8–9, 2021, proceedings (pp. 51–72).
Smeets, M. (2018). The strategic promise of offensive cyber operations. Strategic Studies Quarterly, 12(3), 90–113.
Wendt, D. (2019). Addressing both sides of the cybersecurity equation. Journal of Cyber Security and Information Systems, 7(2).
White House. (2022). Analytical perspectives: Budget of the u.s. government fiscal year 2023. PDF. Retrieved from https:// www.whitehouse.gov/wp-content/uploads/2022/03/ap_16_it_fy2023.pdf
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