Padur, Kart;
Borrion, Herve;
Hailes, Stephen;
(2025)
Using Agent-Based Modelling and Reinforcement Learning to Study Hybrid Threats.
Journal of Artificial Societies and Social Simulation
, 28
(1)
, Article 1. 10.18564/jasss.5539.
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Abstract
Hybrid attacks coordinate the exploitation of vulnerabilities across domains to undermine trust in authorities and cause social unrest. Whilst such attacks have primarily been seen in active conflict zones, there is growing concern about the potential harm that can be caused by hybrid attacks more generally and a desire to discover how better to identify and react to them. In addressing such threats, it is important to be able to identify and understand an adversary's behaviour. Game theory is the approach predominantly used in security and defence literature for this purpose. However, the underlying rationality assumption, the equilibrium concept of game theory, as well as the need to make simplifying assumptions can limit its use in the study of emerging threats. To study hybrid threats, we present a novel agent-based model in which, for the first time, agents use reinforcement learning to inform their decisions. This model allows us to investigate the behavioural strategies of threat agents with hybrid attack capabilities as well as their broader impact on the behaviours and opinions of other agents. In this paper, we demonstrate the face validity of this approach and argue that its generality and adaptability render it an important tool in formulating holistic responses to hybrid threats, including proactive vulnerability identification, which does not necessarily emerge by considering the multiple threat vectors independently.
Type: | Article |
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Title: | Using Agent-Based Modelling and Reinforcement Learning to Study Hybrid Threats |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.18564/jasss.5539 |
Publisher version: | https://www.jasss.org/28/1/1.html |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Hybrid Threats, Agent-Based Modelling, Reinforcement Learning, Cyberattack, Misinformation |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10203936 |
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