12 papers
Optimal Stopping of Self-Refining Foundation Models
Kim Hammar, Tansu Alpcan, Emil C. Lupu
Foundation models can improve their outputs through a self-refinement process driven by external feedback. In this process, the model is embedded in an iterative loop where it gene…
Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning
Yiran Gao, Tao Li, Kim Hammar
Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making processes. Consequently, there…
Recovery Control in Replicated Systems through Autonomous Multiagent Rollout
Kim Hammar, Yuchao Li
We study recovery control in replicated computing systems. Such systems consist of replicas that collectively provide a service to a client population. This redundancy enables the…
CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Kim Hammar
Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement learning solutions for security m…
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…
Hallucination-Resistant Security Planning with a Large Language Model
Kim Hammar, Tansu Alpcan, Emil Lupu
Large language models (LLMs) are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucin…
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…
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…
Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Kim Hammar, Tansu Alpcan, Emil C. Lupu
Timely and effective incident response is key to managing the growing frequency of cyberattacks. However, identifying the right response actions for complex systems is a major tech…
Feature-Based Belief Aggregation for Partially Observable Markov Decision Problems
Yuchao Li, Kim Hammar, Dimitri Bertsekas
We consider a finite-state partially observable Markov decision problem (POMDP) with an infinite horizon and a discounted cost, and we propose a new method for computing a cost fun…
Adaptive Network Security Policies via Belief Aggregation and Rollout
Kim Hammar, Yuchao Li, Tansu Alpcan +2
Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response…
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…