Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Hammar, Kim"'
The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) reinforcement learning, which does not provid
Externí odkaz:
http://arxiv.org/abs/2407.11070
Autor:
Hammar, Kim, Stadler, Rolf
We formulate intrusion tolerance for a system with service replicas as a two-level optimal control problem. On the local level node controllers perform intrusion recovery, and on the global level a system controller manages the replication factor. Th
Externí odkaz:
http://arxiv.org/abs/2404.01741
Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium devi
Externí odkaz:
http://arxiv.org/abs/2402.18781
We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified a
Externí odkaz:
http://arxiv.org/abs/2402.12499
Autor:
Hammar, Kim, Stadler, Rolf
Publikováno v:
International Conference of Decision and Game Theory for Security (GameSec) 2023, pp 172-192
We study automated intrusion response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed stochastic game. To solve the game we follow an approach where attack and defense strategies co-ev
Externí odkaz:
http://arxiv.org/abs/2309.03292
Autor:
Hammar, Kim, Dhir, Neil
We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation
Externí odkaz:
http://arxiv.org/abs/2309.02287
Autor:
Hammar, Kim, Stadler, Rolf
Publikováno v:
IEEE Transactions on Network and Service Management ( Volume: 21, Issue: 1, February 2024)
We study automated intrusion response and formulate the interaction between an attacker and a defender as an optimal stopping game where attack and defense strategies evolve through reinforcement learning and self-play. The game-theoretic modeling en
Externí odkaz:
http://arxiv.org/abs/2301.06085
Autor:
Hammar, Kim, Stadler, Rolf
We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the interaction between an attacker and a defender as an optimal stopping game and let attack and defense strategies evolve through reinfor
Externí odkaz:
http://arxiv.org/abs/2205.14694
Autor:
Hammar, Kim, Stadler, Rolf
Publikováno v:
NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software deb
Externí odkaz:
http://arxiv.org/abs/2204.01126
Autor:
Hammar, Kim, Stadler, Rolf
Publikováno v:
IEEE Transactions on Network and Service Management ( Volume: 19, Issue: 3, September 2022)
We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping problem. This formulation gives us insight into the structure of opti
Externí odkaz:
http://arxiv.org/abs/2111.00289