174 citations · 356 across the 14 of their papers we have counts for
14 papers
Online Identification of IT Systems through Active Causal Learning
Kim Hammar, Rolf Stadler
Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions,…
Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search
Kim Hammar, Neil Dhir, Rolf Stadler
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…
Intrusion Tolerance for Networked Systems through Two-Level Feedback Control
Kim Hammar, Rolf Stadler
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 t…
Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
Tao Li, Kim Hammar, Rolf Stadler +1
Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods…
Automated Security Response through Online Learning with Adaptive Conjectures
Kim Hammar, Tao Li, Rolf Stadler +1
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 rela…
Scalable Learning of Intrusion Responses through Recursive Decomposition
Kim Hammar, Rolf Stadler
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 th…
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
Kim Hammar, Neil Dhir
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 st…
Learning Near-Optimal Intrusion Responses Against Dynamic Attackers
Kim Hammar, Rolf Stadler
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 throug…
Learning Security Strategies through Game Play and Optimal Stopping
Kim Hammar, Rolf Stadler
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 stop…
A System for Interactive Examination of Learned Security Policies
Kim Hammar, Rolf Stadler
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…
Intrusion Prevention through Optimal Stopping
Kim Hammar, Rolf Stadler
We study automated intrusion prevention using reinforcement learning. Following a novel approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping…
Learning Intrusion Prevention Policies through Optimal Stopping
Kim Hammar, Rolf Stadler
We study automated intrusion prevention using reinforcement learning. In a novel approach, we formulate the problem of intrusion prevention as an optimal stopping problem. This for…
Finding Effective Security Strategies through Reinforcement Learning and Self-Play
Kim Hammar, Rolf Stadler
We present a method to automatically find security strategies for the use case of intrusion prevention. Following this method, we model the interaction between an attacker and a de…
Deep Text Mining of Instagram Data Without Strong Supervision
Kim Hammar, Shatha Jaradat, Nima Dokoohaki +1
With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving use…