IoT Risk Detect is a free and open-source (FOSS) desktop tool for real-time discovery, risk assessment, and botnet/anomaly detection of IoT devices on your local network. It empowers users, researchers, and defenders to:
- Discover all IoT devices on their network
- Assess risk (open ports, vendor, suspicious MACs)
- Detect botnet-like behavior (heuristics + ML anomaly detection)
- Export and analyze results for further action
No cloud, no vendor lock-in, no data collection. 100% local, 100% FOSS.
- Network scan: ARP-based device discovery
- Risk assessment: Open ports, vendor, MAC analysis
- Botnet detection:
- Heuristic (bad IPs, port/protocol, external IPs)
- ML-based (Isolation Forest anomaly detection)
- Detailed device view: Double-click for all info & risk reasons
- Export: Save results (CSV) for compliance or research
- Modern GUI: PyQt5, color-coded, status bar, responsive
- No database, no server, no web UI
- Clone the repo:
git clone https://github.com/flatmarstheory/iot-risk-detect.git cd iot-risk-detect - Install dependencies:
pip install -r requirements.txt
- Run the app:
python main.py
Run as administrator/root for full network scan capability.
- Build the image:
docker build -t iot-risk-detect . - Run the app:
docker run --rm -it --net=host --env DISPLAY=$DISPLAY \ -v /tmp/.X11-unix:/tmp/.X11-unix iot-risk-detectYou must allow X11 forwarding for GUI apps in Docker. See docs for Windows/Mac.
- PRs, issues, and feature requests welcome!
- See CONTRIBUTING.md for guidelines.
- Attribution: flatmarstheory
MIT License. See LICENSE.
- 100% open source, no telemetry, no vendor lock-in
- Designed for researchers, defenders, and privacy advocates
- Use at your own risk. For educational and defensive purposes only.
- GitHub: flatmarstheory/iot-risk-detect
- Sponsor: Buy Me a Coffee