TY - GEN
T1 - Coalition Obstruction Temporal Logic
T2 - 34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025
AU - Catta, Davide
AU - Leneutre, Jean
AU - Malvone, Vadim
AU - Ortiz, James
N1 - Publisher Copyright:
© 2025 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - In multi-agent systems, especially in cybersecurity, the dynamic interplay between attackers and defenders is crucial to the security and resilience of the system. Traditional methods often assume static game models and fail to account for the strategic adaptation of the environment to the actions of the players. This paper presents Coalition Obstruction Temporal Logic (COTL), a formal framework for analyzing defender coalitions in dynamic game scenarios. Within this framework, defenders, conceptualized as demons, can actively obstruct attackers by selectively disabling certain actions in response to perceived threats. We establish the formal semantics of COTL and propose a model-checking algorithm to verify complex security properties in systems with evolving adversarial dynamics. The utility of the framework is demonstrated through its application to a coalition of defenders that collaboratively defend a system against coordinated attacks.
AB - In multi-agent systems, especially in cybersecurity, the dynamic interplay between attackers and defenders is crucial to the security and resilience of the system. Traditional methods often assume static game models and fail to account for the strategic adaptation of the environment to the actions of the players. This paper presents Coalition Obstruction Temporal Logic (COTL), a formal framework for analyzing defender coalitions in dynamic game scenarios. Within this framework, defenders, conceptualized as demons, can actively obstruct attackers by selectively disabling certain actions in response to perceived threats. We establish the formal semantics of COTL and propose a model-checking algorithm to verify complex security properties in systems with evolving adversarial dynamics. The utility of the framework is demonstrated through its application to a coalition of defenders that collaboratively defend a system against coordinated attacks.
UR - https://www.scopus.com/pages/publications/105021801022
U2 - 10.24963/ijcai.2025/3
DO - 10.24963/ijcai.2025/3
M3 - Conference contribution
AN - SCOPUS:105021801022
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 21
EP - 28
BT - Proceedings of the 34th International Joint Conference on Artificial Intelligence, IJCAI 2025
A2 - Kwok, James
PB - International Joint Conferences on Artificial Intelligence
Y2 - 16 August 2025 through 22 August 2025
ER -