Security Operations Tools
Mean Time to Detect Calculator
Enter security event count and sum of detection times to compute mean time to detect, dwell time, and benchmark against industry averages.
Calculations run locally in your browserTool
Example — 10 detected events · 250h total · 2 missed incidents · benchmark: 14d (Mandiant M-Trends 2026)
MTTD
25.0 hours
avg across 10 detected events
vs industry benchmark
✓ Better than 14d benchmark
Mandiant M-Trends 2026 (ratio: 0.07×)
Detection rate
83%
2 missed incidents
Missed incident risk
⚠ Improve detection coverage
About this tool
Mean Time to Detect Calculator
The Mean Time to Detect Calculator computes MTTD, dwell time, and detection rate from event data, using the Mandiant M-Trends 2026 global median dwell time (14 days) as the industry benchmark.
• Calculate MTTD for a quarterly security metrics report
• Benchmark current MTTD against the Mandiant M-Trends 2026 14-day median dwell time
• Track MTTD improvement trend from a detection engineering investment
• Identify whether MTTD is within target for a security SLO
Affiliate disclosure
Uptime, incident, and on-call management. Better Stack provides status pages, incident management, and on-call scheduling for engineering teams.
View detection with Better Stack
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FAQ
What does this tool tell you?
The Mean Time to Detect Calculator computes MTTD, dwell time, and detection rate from event data, using the Mandiant M-Trends 2026 global median dwell time (14 days) as the industry benchmark.
What affects the result most?
MTTD: time from initial compromise to detection. Mandiant M-Trends 2026 global median dwell time = 14 days (calendar 2025 investigations); refresh annually when the next M-Trends edition publishes. MTTR for security: time from detection to full remediation — separate from MTTD and tracked in a different tool. Dwell time and MTTD are the same metric framed differently: dwell time emphasizes the attacker's presence, MTTD emphasizes the defender's gap.
How should I use the result?
The calculation is deterministic — the same inputs always produce the same output — so the most useful workflow is to vary one input at a time and see which factor moves the result most. That tells you where to focus your attention before committing to a decision.