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A framework for building and evaluating ML/DL based, staged (cascade) network intrusion detection systems.

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IDSLab

hashtagRR.github.io/IDSLab

PyPI DOI

IDSLab overview. Question: does every stage of your IDS cascade earn its place? 1 Your design: a binary classifier, a router and an anomaly gate, each with its model and decision, written in one YAML file. 2 One fixed protocol: splits per seed with TEST locked, each stage fitted on its own rows, models and thresholds chosen on VAL with off as an option, TEST read once next to controls. 3 The answer on synthetic data: the cascade and the first stage alone reach almost the same TEST accuracy; the entry classifier carries the result and the router and gate add nothing here.

A framework for building and evaluating staged (cascade) network intrusion detection systems. You describe a design in one YAML file, or draw it in the dashboard: the stages, the model in each, and where each stage sends the flows it judges. IDSLab fits it, makes every choice on validation data, and then shows on untouched test data whether the design beats its own first stage alone, and what each stage adds.

Quickstart

IDSLab needs Python 3.10 or newer and installs from PyPI.

python3 -m venv .venv && .venv/bin/pip install idslab
.venv/bin/idslab example demo                                  # synthetic data and three reference designs
.venv/bin/idslab run demo/r1_router_gate.yaml --out demo/runs  # fit, choose on VAL, report on TEST
.venv/bin/idslab dashboard --workspace demo --open             # optional web UI

On Windows, in PowerShell (the programs are in .venv\Scripts):

py -3.12 -m venv .venv; .\.venv\Scripts\pip install idslab
.\.venv\Scripts\idslab example demo
.\.venv\Scripts\idslab run demo\r1_router_gate.yaml --out demo\runs
.\.venv\Scripts\idslab dashboard --workspace demo --open   # keep the window open; Ctrl+C stops it

The report is demo/runs/r1-router-gate/report.html. Step-by-step guides for Windows, macOS and GNU/Linux, optional model libraries and troubleshooting are in INSTALL.md.

Documentation

Guide What it covers
How it works one run in figures: the design, the splits, the accuracy after each stage, the frontier gap and the flow map
Getting started a first run on the synthetic data, reading the report, moving to your own data
Example designs the three reference cascades with diagrams, a custom model adapter, an outside score job
Config reference every key of a config file, with defaults
Protocol and controls splits, greedy selection on VAL, the controls and what each reported number means
Models in stages score contracts, pipelines and search, PyTorch, your own adapter, scores from models trained elsewhere
Outputs the report pages and every file a run writes
Comparing designs and sweeps two designs on the same flows, paired per seed; one base config with many variants
Command line validate, run, report, export-splits, example, compare, sweep, dashboard
Dashboard the local web UI: pages, jobs, security (source reference)
Architecture what happens during a run, the modules, and where to extend
Known limitations what the numbers do and do not show
Design notes the reasoning behind each protocol rule
Install guide platform setup, extras, tests and checks
Changelog what changed in each version
Project site the project's overview page

Versions

Every release and its changes are in the changelog and on the Releases page. Releases from 0.3.0 on are archived on Zenodo under the concept DOI above. From 0.3.1 on, a release that does not change the evaluation keeps finished runs; upgrades to 0.2.0 and 0.3.0 ran them again.

  • v0.3.0: the dashboard redesigned around experiments, with a guided setup, a results page that reads the cascade against its first stage, and job progress.
  • v0.2.0: visual pipeline builder, cost objectives and stage cost, plugins, idslab compare and idslab sweep.
  • v0.1.0: first public release.

Citing

Cite the version you used. The concept DOI 10.5281/zenodo.23435536 always resolves to the latest version, and lists each version's own DOI and citation. CITATION.cff holds the same details, and GitHub shows them under "Cite this repository".

License

AGPL-3.0-or-later. See LICENSE.

About

A framework for building and evaluating ML/DL based, staged (cascade) network intrusion detection systems.

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