StackelbergEquilibribumSolvers

This package was first made in the winter of 2015 in the state of Tempe at Arizona State University when I was working on a paper for AAMAS, 2016.

See run.sh in the src/DOBSS folders to see how an example input.txt can be run.

Strategy generation code for web-applications [paper]:

cd ./src/DOBSS
python BSG_miqp.py mtd_webapps_input

Strategy generation code for web-applications that handles switching costs [paper]

cd ./src/switch_cost_DOBSS
python cost_BSG_miqp.py cost_BSSG_input.txt

Strategy generation code for IDS placement [paper]

cd ./src/ResourcesHomogeneousScheduleSingleton
python BSG_multi_lp.py BSSG_input.txt

The above code provides you with the marginal probabilities. Use the following code to get the mixed strategy distribution (Uses code by Aubrey Clark).

python strategy_generator.py

Strategy generation code for deep neural networks [paper], use the following command:

cd ./src/DOBSS
python BSG_miqp.py mtd_neuralnets_input