Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Stadler, Rolf"'
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
Autor:
Wang, Xiaoxuan, Stadler, Rolf
We study automated intrusion detection in an IT infrastructure, specifically the problem of identifying the start of an attack, the type of attack, and the sequence of actions an attacker takes, based on continuous measurements from the infrastructur
Externí odkaz:
http://arxiv.org/abs/2402.13081
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:
Samani, Forough Shahab, Stadler, Rolf
We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to r
Externí odkaz:
http://arxiv.org/abs/2306.14178
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:
Samani, Forough Shahab, Stadler, Rolf
We present a framework that lets a service provider achieve end-to-end management objectives under varying load. Dynamic control actions are performed by a reinforcement learning (RL) agent. Our work includes experimentation and evaluation on a labor
Externí odkaz:
http://arxiv.org/abs/2210.04002
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