From the 1 of 12 linked papers with an AI index.
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
The paper formulates the timing of replica recovery in redundant computing systems as a multi‑agent POMDP and proposes a multi‑agent rollout approach that uses precomputed signalin…
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…
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…
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…