Publications (26)
In-Context Autonomous Network Incident Response: An End-to-End Large Language Model Agent Approach
Yiran Gao, Kim Hammar, Tao Li
Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively explored the reinforcement lear…
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…
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…
Causal Online Learning of Safe Regions in Cloud Radio Access Networks
Kim Hammar, Tansu Alpcan, Emil Lupu
Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to i…
Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization
Kim Hammar, Tao Li
Effective responses to cyberattacks require fast decisions, even when information about the attack is incomplete or inaccurate. However, most decision-support frameworks for incide…
Optimal Security Response to Network Intrusions in IT Systems
Kim Hammar
Cybersecurity is one of the most pressing technological challenges of our time and requires measures from all sectors of society. A key measure is automated security response, whic…