1) Add Wi‑Fi sample & predict
Feature vector is automatically derived from these inputs.
Tip: Toggle “Encryption mismatch” to simulate when an AP claims a different security type than expected. The
model uses signal strength, channel, and known channels/BSSIDs to inform its inference.
2) Real-time threat monitor
Simulated environment: random APs appear; malicious ones are generated by a hidden adversary policy.
Total scanned
0
Detections (evil twin)
0
Detection rate
0%
Last risk
—
Detection logic uses the trained model score with a calibrated threshold of 0.50. In production, tune the
threshold with a validation set to balance false positives/negatives.
3) Train the model (on synthetic data)
The dataset is generated in-browser and based on plausible Evil Twin behaviors.
This demo runs fully in your browser using TensorFlow.js. No network scans are performed; it simulates Wi‑Fi
features for training and prediction. For a production deployment, feed real measurements (e.g., BSSID,
signal, channel) from your platform’s Wi‑Fi stack, and monitor over time for anomalies.