Implementing Continuous Authentication
1. Understanding Continuous Authentication
| Concept | Detail |
|---|
| Beyond login | Verify identity throughout session |
| Signals | Behavioral, contextual, biometric |
| Action | Re-auth, step-up, or terminate on anomaly |
| Standard | OIDC CAEP, Shared Signals Framework |
2. Implementing Behavior Monitoring
| Signal | Detail |
|---|
| Navigation pattern | Pages visited, order |
| API call rate | Spike detection |
| Session duration | Unusual length |
| Geolocation drift | Mid-session country change |
3. Using Behavioral Biometrics
| Signal | Detail |
|---|
| Keystroke dynamics | Dwell + flight times |
| Mouse movement | Trajectory, acceleration |
| Touch gestures | Pressure, swipe speed (mobile) |
| Vendors | BioCatch, BehavioSec |
4. Implementing Mouse and Keystroke Analysis
Example: Feature collection
const events = [];
document.addEventListener("keydown", e => events.push({ k: e.key, t: performance.now(), type: "d" }));
document.addEventListener("keyup", e => events.push({ k: e.key, t: performance.now(), type: "u" }));
// Periodically POST features (dwell/flight) to risk engine
5. Using Session Risk Scoring
| Aspect | Detail |
|---|
| Dynamic | Score updates per event |
| Decay | Old anomalies fade over time |
| Threshold | Cross → trigger response |
6. Detecting Session Anomalies
| Anomaly | Action |
|---|
| IP change | Re-auth required |
| UA change | Force re-login (session hijack) |
| Concurrent session in 2 countries | Terminate older |
7. Implementing Re-Authentication Triggers
| Trigger | Action |
|---|
| Risk > threshold | Prompt MFA |
| Sensitive action | auth_time check; re-auth if stale |
| Long idle | Soft re-auth (password or biometric) |
8. Using Context-Aware Session Validation
Re-evaluate session validity using current context (network, device, time) on each request — not just at login.
9. Implementing Machine Learning for Continuous Auth
| Approach | Detail |
|---|
| Per-user baseline | One-class SVM, autoencoder |
| Online learning | Update model from confirmed activity |
| Privacy | On-device inference where possible |
10. Handling Security vs UX Trade-Offs
Warning: Over-aggressive challenges drive users away. Tune thresholds with A/B testing; track friction-to-fraud-prevention ratio.