5 papers
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…
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…
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…