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Calcrivo

Scanner False Positive Rate Calculator

Turn triage outcomes into false-positive rate, precision, recall and F1, plus the hours and money spent chasing noise.

Inputs

findings
findings
findings

From penetration tests, bug bounty or a second scanner

minutes
currency/hour

False Positive Rate

30.36%

Precision

69.64%

Recall

95.12%

F1 Score

80.41%

Hours Spent on False Positives

68.0hours

Annualised Cost of the Noise

$53,040.00

Interpretation

High noise — tune plugins and suppress the top repeat offenders

Step by step

  1. Values used

    Confirmed true positives = 780 findings; Confirmed false positives = 340 findings; Known findings the scanner missed = 40 findings; Triage time per finding = 12 minutes; Analyst cost = 65 currency/hour

  2. Scanner False Positive Rate

    False positive rate = FP ÷ (TP + FP); precision = TP ÷ (TP + FP); recall = TP ÷ (TP + FN); F1 = 2 × precision × recall ÷ (precision + recall).

  3. Cost of noise

    Wasted hours = FP × triage minutes ÷ 60, and the annualised cost multiplies one cycle's waste by twelve.

  4. False Positive Rate

    = 30.36

  5. Precision

    = 69.64

  6. Recall

    = 95.12

  7. F1 Score

    = 80.41

  8. Hours Spent on False Positives

    = 68.0 hours

  9. Annualised Cost of the Noise

    = 53,040.00

How it works

Precision and false-positive rate are complements of the same quantity — reporting both makes the trade-off obvious when someone proposes loosening detection to find more. Recall needs an external truth source, so the false-negative count has to come from penetration tests, a second scanner or bug bounty reports; without it, a quiet scanner looks perfect. A scanner with a 40% false-positive rate does not just waste time — it trains your engineers to dismiss findings, which is how the one real critical in the batch gets closed unread.

Formulas

Scanner False Positive Rate

False positive rate = FP ÷ (TP + FP); precision = TP ÷ (TP + FP); recall = TP ÷ (TP + FN); F1 = 2 × precision × recall ÷ (precision + recall).

TP
Findings confirmed real
FP
Findings confirmed not real
FN
Real findings the scanner did not report
F1
Harmonic mean of precision and recall

Cost of noise

Wasted hours = FP × triage minutes ÷ 60, and the annualised cost multiplies one cycle's waste by twelve.

Frequently Asked Questions

How is Scanner False Positive Rate calculated?

False positive rate = FP ÷ (TP + FP); precision = TP ÷ (TP + FP); recall = TP ÷ (TP + FN); F1 = 2 × precision × recall ÷ (precision + recall). Precision and false-positive rate are complements of the same quantity — reporting both makes the trade-off obvious when someone proposes loosening detection to find more. Recall needs an external truth source, so the false-negative count has to come from penetration tests, a second scanner or bug bounty reports; without it, a quiet scanner looks perfect.

Why does Scanner False Positive Rate matter?

A scanner with a 40% false-positive rate does not just waste time — it trains your engineers to dismiss findings, which is how the one real critical in the batch gets closed unread.

What values do I need to enter?

This calculator takes 5 inputs: Confirmed true positives, Confirmed false positives, Known findings the scanner missed, Triage time per finding, Analyst cost. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.

Is a low false-positive rate always good?

Not if it was bought with recall. A scanner tuned to report only what it can prove will miss authenticated-only and configuration findings entirely. F1 is the figure to trend, because it punishes both failure modes.

What do I do about repeat false positives?

Suppress by rule, not by finding, and give every suppression an owner, a reason and an expiry date. Unexpiring suppressions become invisible risk acceptances, and they are the first thing worth auditing when coverage looks suspiciously clean.

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