diff --git "a/static/index.html" "b/static/index.html" --- "a/static/index.html" +++ "b/static/index.html" @@ -1,1448 +1,1570 @@ - +
- - -Test all framework features: JWT Auth, 2FA, Risk Assessment, Session Monitoring & Admin Dashboard
- - -Production-ready risk-based auth — JWT • 2FA • Behavioral Analysis • Anomaly Detection
+Creates the demo user with a realistic 30-day behavioral profile (15 logins from a trusted IP, device, and time window).
+Why was this required?
+AnomalyDetector fire in real time,
+ then show how a legitimate user is treated vs the attacker with the correct password.
+ Injects failed login attempts from attacker IP 192.0.2.100 (Beijing, China), then triggers the AnomalyDetector.
- Problem Statement: "Dynamically adjust security requirements based on risk assessment" -
- -Same device, same IP, same browser = Low risk
-Different device fingerprint = Medium risk
-Multiple failed attempts = Blocked
-View how risk factors are calculated
- - -| Level 0 | -Trusted (Known device + IP + browser) | -Password only | -
| Level 1 | -Unknown browser | -Password required | -
| Level 2 | -Unknown IP | -Email verification | -
| Level 3 | -Unknown device | -2FA required | -
| Level 4 | -Suspicious pattern | -BLOCKED | -
No anomalies yet. Run the attack simulation above.
Retrieve current user profile information
- - -Same account. Logs in from New York (trusted IP, trusted device) while the attack is in progress.
+ +Test basic JWT authentication
- - -Test admin role access (requires admin token)
- - -The attacker somehow obtained the real password. See what happens.
Spoiler: correct password alone is not enough.
Generate QR code and setup 2FA
- - - -Remove two-factor authentication
- - -- These functions require admin privileges. Login with admin credentials. -
-Test JWT-protected routes. Must have a valid token saved.
+demo.admin@adaptive.demo / Admin@Demo456! first.
+ demo.admin@adaptive.demo / Admin@Demo456!
+
+ + Challenges fire only when trust drops below 40 — never interrupts a trusted session. +
++ Submit factor scores and see exactly which signals contributed and why — with model weights. +
+