Getting started

git clone https://github.com/rakshit-737/feint-adversarial-ids && cd feint
pip install -e ".[dev]"                 # numpy, pandas, scikit-learn, xgboost (+ matplotlib, fastapi for dev)
python -m pytest -q                     # real-data tests skip without datasets
python -m feint run --quick --n 3000 --out results/demo   # 1-minute synthetic smoke study

Score flows over HTTP with explanations (after a run with --save-model):

python -m feint serve --model results/unsw_nb15/model.joblib
curl -s localhost:8000/score -H 'content-type: application/json' \
  -d '{"explain": true, "flows": [{"dur": 0.00001, "spkts": 2, "dpkts": 0, "sbytes": 114, "sttl": 254, "proto_udp": 1}]}'

Reproducing the benchmarks

make is optional; every target is a plain command.

step	command	time (laptop CPU, shared with other jobs)
download data	python scripts/download_unsw_nb15.py && python scripts/download_cicids2017.py (set FEINT_DATA to choose the directory, default ./data)	network bound
UNSW-NB15 study	python -m feint run --data unsw_nb15 --out results/unsw_nb15 --save-model	~65 min (3,776 s recorded)
CIC-IDS2017 study	python -m feint run --data cicids2017 --out results/cicids2017	~60 min (+7 min first CSV load; cached after)
real-data tests	FEINT_DATA=./data python -m pytest -q -m realdata

Key knobs: --eps, --iters (black-box queries), --steps (PGD), --adv-rounds, --max-eval, --frac (CIC sampling), --seed.

Multi-seed study

python -m feint seeds --data unsw_nb15 --seeds 0 1 2 --out results/unsw_nb15
python -m feint seeds --data cicids2017 --seeds 0 1 2 --out results/cicids2017

Docker

docker run --rm ghcr.io/rakshit-737/feint-adversarial-ids:latest run --quick --n 2000 --out /tmp/demo
docker compose up api   # serves results/unsw_nb15/model.joblib on :8000 (train it first with --save-model)