/TNSM_Learning_IRS_Supplementary

Supplementary material for "Learning Near-Optimal Intrusion Responses Against Dynamic Attackers" by Hammar & Stadler, 2023, IEEE TNSM

Primary LanguagePythonOtherNOASSERTION

Supplementary material

This repository contains supplementary material for the publication "Learning Near-Optimal Intrusion Responses Against Dynamic Attackers", Hammar & Stadler, to appear in IEEE TNSM 2023. It contains scripts and commands for implementing the emulated attacker, defender, and client population as described in the paper.

For details about th ecommands, see the PDF file.

The Docker images with the vulnerabilities and services that the commands relate to are available here

Copyright and license

Creative Commons (C) 2023, Kim Hammar, Rolf Stadler