Abstract
Autonomous Intelligence Cyber-defense Agents are expected to operate in a continuous, unmanned, collaborative capacity in a variety of target network or battlefield environments. They should be able to maintain situational awareness of the nature of the cyber environment and other “agents” within it, monitor for activity that presents a potential threat or advantage, incorporate new knowledge into their environmental model, share parameters of such a model with peers, and take appropriate actions to maximize their own mission success and/or survival (potentially in a collaborative manner). In this chapter, we analyze several scenarios to consider the types of threats such agents might be expected to encounter and what actions would potentially be beneficial for them to take in response. These scenarios include an unmanned automated system (UAS, or “drone”) – solo or as part of a swarm, an electrical distribution grid, an orbital or deep-space communication network, and a large-scale computational array (such as found in a cloud vendor offering or high-performance computing).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbas, H. A., Shaheen, S. I., & Amin, M. H. (2015). On the adoption of multi-agent systems for the development of industrial control networks. ArXiv:1506.05235 [Cs]. http://arxiv.org/abs/1506.05235
Andreadis, G., Klazoglou, P., Niotaki, K., & Bouzakis, K.-D. (2014). Classification and review of multi-agents Systems in the Manufacturing Section. Procedia Engineering, 69, 282–290. https://doi.org/10.1016/j.proeng.2014.02.233
Avgerinos, T., Brumley, D., Davis, J., Goulden, R., Nighswander, T., Rebert, A., & Williamson, N. (2018). The Mayhem cyber reasoning system. IEEE Security & Privacy, 16(2), 52–60. https://doi.org/10.1109/MSP.2018.1870873
Booker, L. B., & Musman, S. A. (2020). A model-based, decision-theoretic perspective on automated cyber response. ArXiv:2002.08957 [Cs]. http://arxiv.org/abs/2002.08957
Cam, H. (2020). Cyber resilience using autonomous agents and reinforcement learning. In T. Pham, L. Solomon, & K. Rainey (Eds.), Artificial intelligence and machine learning for multi-domain operations applications II (p. 35). SPIE. https://doi.org/10.1117/12.2559319
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020). Reinforcement learning and its applications in modern power and energy systems: A review. Journal of Modern Power Systems and Clean Energy, 8(6), 1029–1042. https://doi.org/10.35833/MPCE.2020.000552
David, R. A., & Nielsen, P. (2016). Defense science board summer study on autonomy. Defense Science Board Washington United States. https://apps.dtic.mil/sti/citations/AD1017790.
Davidson, C., & Andel, T. (2016). Feasibility of applying moving target defensive techniques in a SCADA system. In 11th international conference on cyber warfare and security. https://doi.org/10.13140/RG.2.1.5189.5441.
Hammar, K., & Stadler, R. (2020). Finding effective security strategies through reinforcement learning and self-play. ArXiv:2009.08120 [Cs, Stat]. https://doi.org/10.13140/RG.2.2.14128.38405.
Han, Y., Rubinstein, B. I. P., Abraham, T., Alpcan, T., De Vel, O., Erfani, S., Hubczenko, D., Leckie, C., & Montague, P. (2018). Reinforcement learning for autonomous defence in software-defined networking. In L. Bushnell, R. Poovendran, & T. Başar (Eds.), Decision and game theory for security (pp. 145–165). Springer International Publishing. https://doi.org/10.1007/978-3-030-01554-1_9
Holland, O. T. (n.d.). Taxonomy for the modeling and simulation of emergent. Behavior Systems, 1, 9.
Hu, Y., Zhu, P., Xun, P., Liu, B., Kang, W., Xiong, Y., & Shi, W. (2021). CPMTD: Cyber-physical moving target defense for hardening the security of power system against false data injected attack. Computers & Security, 111, 102465. https://doi.org/10.1016/j.cose.2021.102465
Huang, L., & Zhu, Q. (2018). Analysis and computation of adaptive Defense strategies against advanced persistent threats for cyber-physical systems. In L. Bushnell, R. Poovendran, & T. Başar (Eds.), Decision and game theory for security (pp. 205–226). Springer International Publishing. https://doi.org/10.1007/978-3-030-01554-1_12
Jin, H., Li, Z., Zou, D., & Yuan, B. (2021). DSEOM: A framework for dynamic security evaluation and optimization of MTD in container-based cloud. IEEE Transactions on Dependable and Secure Computing, 18(3), 1125–1136. https://doi.org/10.1109/TDSC.2019.2916666
Kotenko, I., Konovalov, A., & Shorov, A. (2012). Agent-based simulation of cooperative defence against botnets. Concurrency and Computation: Practice and Experience, 24(6), 573–588. https://doi.org/10.1002/cpe.1858
Kott, A., & Theron, P. (2020). Doers, not watchers: Intelligent autonomous agents are a path to cyber resilience. IEEE Security & Privacy, 18(3), 62–66. https://doi.org/10.1109/MSEC.2020.2983714
Kott, A., Golan, M. S., Trump, B. D., & Linkov, I. (2021). Cyber resilience: By design or by intervention? Computer, 54(8), 112–117. https://doi.org/10.1109/MC.2021.3082836
Ligo, A. K., Kott, A., & Linkov, I. (2021). Autonomous Cyberdefense introduces risk: Can we manage the risk? Computer, 54(10), 106–110. https://doi.org/10.1109/MC.2021.3099042
Linkov, I., Galaitsi, S., Trump, B. D., Keisler, J. M., & Kott, A. (2020). Cybertrust: From explainable to actionable and interpretable artificial intelligence. Computer, 53(9), 91–96. https://doi.org/10.1109/MC.2020.2993623
Maier, M. W. (2014). The role of Modeling and simulation in system of systems development. In Modeling and simulation support for system of systems engineering applications (pp. 11–41). Wiley. https://doi.org/10.1002/9781118501757.ch2
Pappa, A., Ashok, A., & Govindarasu, M. (2017). Moving target Defense for security smart grid communications: Architecture, Implementation & Evaluation. In Power & Energy Society Innovative Smart Grid Technologies Conference (pp. 3–7). https://doi.org/10.1109/ISGT.2017.8085954
Pawlick, J., Colbert, E., & Zhu, Q. (2019). A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy. ACM Computing Surveys, 52(4), 1–28. https://doi.org/10.1145/3337772
Prosser, B. J., & Fulp, E. W. (2020). A distributed population management approach for Mobile agent systems. In 2020 IEEE international conference on autonomic computing and self-organizing systems (ACSOS) (pp. 102–108). https://doi.org/10.1109/ACSOS49614.2020.00031
Rauf, U., Mohsin, M., & Mazurczyk, W. (2019). Cyber regulatory networks: Towards a bio-inspired auto-resilient framework for cyber-Defense. In A. Compagnoni, W. Casey, Y. Cai, & B. Mishra (Eds.), Bio-inspired information and communication technologies (pp. 156–174). Springer International Publishing. https://doi.org/10.1007/978-3-030-24202-2_12
van Dijk, M., Juels, A., Oprea, A., & Rivest, R. L. (2013). FlipIt: The game of “stealthy takeover.”. Journal of Cryptology, 26(4), 655–713. https://doi.org/10.1007/s00145-012-9134-5
Acknowledgements
The work presented in this paper was partially supported by the U.S. Department of Energy, Office of Science under DOE contract number DE-AC02-06CH11357. The submitted manuscript has been created by UChicago Argonne, LLC, operator of Argonne National Laboratory. Argonne, a DOE Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Blakely, B., Horsthemke, W., Harkness, D., Evans, N. (2023). Deployment and Operation. In: Kott, A. (eds) Autonomous Intelligent Cyber Defense Agent (AICA). Advances in Information Security, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-29269-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-29269-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29268-2
Online ISBN: 978-3-031-29269-9
eBook Packages: Computer ScienceComputer Science (R0)