Calculator Inputs
Example Data Table
| Session | Dev % | Mouse | Gesture | Device | IP | Geo | Timing | Fails | MFA | Reset | Score |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A - Known laptop, normal travel | 6 | 88 | 90 | 85 | 80 | 35 | 92 | 0 | Yes | No | 91.4 |
| B - New device, clean behavior | 10 | 82 | 80 | 55 | 75 | 20 | 85 | 1 | Yes | No | 83.0 |
| C - Unusual timing, multiple failures | 18 | 70 | 65 | 70 | 72 | 60 | 40 | 5 | No | No | 66.6 |
| D - Fast geo-velocity, risky IP | 12 | 76 | 74 | 60 | 30 | 820 | 70 | 2 | Yes | Yes | 63.5 |
| E - Bot-like patterns | 45 | 20 | 25 | 40 | 35 | 250 | 30 | 8 | No | Yes | 34.5 |
Formula Used
Each input is converted into a trust score from 0 to 100. Higher trust indicates behavior and context that better match an expected user.
Behavioral Trust Score = ( Σ ( weightᵢ × trustᵢ ) ) ÷ ( Σ weightᵢ )
- Keystroke deviation: trust = 100 − 2 × deviation% (clamped 0–100).
- Geo velocity: 100 (≤50), 80 (≤200), 50 (≤500), 20 (≤1000), else 0.
- Failed attempts: trust = 100 − 10 × attempts (clamped 0–100).
- MFA: trust = 100 if used, else 60.
- Password reset: trust = 70 if recent, else 100.
The calculator also reports Risk Score = 100 − Trust Score, plus an action based on your thresholds.
How to Use This Calculator
- Enter observed session signals or your best estimates.
- Choose a profile, then adjust weights to match your environment.
- Set thresholds for Allow and Step-up to fit your policy.
- Click Calculate Score to see results above the form.
- Export the saved result as CSV or PDF for audits and reviews.
Behavioral signals in practice
Behavioral authentication converts interaction patterns into measurable confidence. In this calculator, keystroke deviation, mouse consistency, and gesture consistency are expressed on a 0–100 trust scale. Organizations commonly observe stable users within 5–15% keystroke deviation, while automated traffic often exceeds 30%. Mouse and gesture consistency above 75 typically indicates repeatable, human-like motion.
Contextual risk enrichment
Context signals catch attacks that mimic behavior. Device trust reflects fingerprint stability and posture, where managed endpoints often score 80–95 and unknown devices trend 40–70. IP reputation incorporates proxy use and threat intelligence; scores below 50 deserve scrutiny. Geo velocity highlights impossible travel; values under 50 km/h are normal, 200–500 km/h suggests travel, and above 1000 km/h is usually anomalous.
Weighted scoring discipline
The score is a weighted average, so weight choices should mirror signal reliability. If your telemetry for IP reputation is noisy, reduce its weight and emphasize interaction signals. Many teams start with total weights between 8 and 12, then calibrate weekly. A simple calibration method is to compare median scores for successful logins versus confirmed fraud, aiming for at least a 15-point separation. For monitoring in production environments, record score percentiles (p50, p90) by cohort and watch drift after UI changes, seasonal travel, or new device rollouts; sudden drops of 10 points often signal telemetry gaps rather than real user risk.
Thresholds and step-up strategy
Decision thresholds translate scores into action. A common policy is Allow at 80, Step-up at 60, and Block below 60, but high-risk apps may shift Allow to 85–90. Step-up can mean MFA, device binding, or re-authentication. Track false challenges: if more than 2–3% of legitimate sessions require step-up, refine weights or improve device trust inputs.
Auditability and privacy controls
Behavioral data is sensitive, so logging should be minimal and purpose-limited. Store the final score, key signal summaries, and decision outcome rather than raw keystrokes or full motion traces. Retention windows of 30–90 days support investigations while reducing exposure. Exportable CSV and PDF outputs simplify reviews, enable sampling, and help document policy changes over time.
FAQs
What does the Behavioral Trust Score represent?
It summarizes how closely a session matches expected user behavior and context on a 0–100 scale. Higher values mean stronger confidence, while lower values indicate abnormal patterns or risky context requiring extra verification.
How should I choose weights for each signal?
Start with balanced defaults, then increase weights for signals you measure reliably and decrease weights for noisy telemetry. Validate changes using confirmed good and bad sessions, aiming for clear separation between their score distributions.
How do I set Allow and Step-up thresholds?
Choose thresholds based on risk tolerance and user impact. Many teams allow at 80 and step-up at 60, then adjust using observed false challenges and incident rates. High-risk transactions typically require higher Allow thresholds.
Why is geo velocity included?
Geo velocity flags logins that imply unrealistic travel between sessions. Very high speeds can indicate credential theft, proxy switching, or session replay. Use it as a contextual check, not a sole blocker, especially for mobile travelers.
What should I store for audits and investigations?
Store the final score, decision, timestamp, and summarized signal values. Avoid storing raw keystrokes or detailed motion traces unless required. Keep retention short, and document weight and threshold changes for defensibility.
How often should baselines and thresholds be reviewed?
Review at least monthly, and after major UI changes, device rollouts, or new threat campaigns. Monitor score drift and challenge rates; consistent shifts usually mean telemetry or population changes that require recalibration.