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The evaluation reported catching 219 of 629 attacks, a pooled rate of 35%. But its four held-out suites ranged from 1% to 100%—a 99-percentage-point spread. The aggregate is useful, but by itself it hides how uneven the reported results were.
What the 35% pooled result measures
A DEV Community post reports that the detector caught 219 attacks among 629 attacks across four held-out suites, which rounds to 35%. That figure combines the catches and attack counts from all four suites. It answers: “What share of the attacks in this combined set were caught?” It does not show how the detector performed in each suite.
The post’s indexed excerpt does not establish the evaluation’s publication date, the full detector setup, or how the suites were constructed. Treat these numbers as results from that reported evaluation, not as a general estimate of detector performance.
How the four suite results differ
| Held-out suite | Attacks caught | Reported rate |
|---|---|---|
| Workspace | 55 of 240 | 23% |
| Travel | 140 of 140 | 100% |
| Banking | 23 of 144 | 16% |
| Slack | 1 of 105 | 1% |
The displayed rates run from 1% in Slack to 100% in travel, a 99-percentage-point range calculated from the post’s rounded figures. The counts make the percentages interpretable: for example, Slack’s 1% represents one catch among 105 attacks, while travel’s 100% represents 140 among 140.
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The aggregate is weighted by the number of attacks in each suite, so it is not the simple average of the four displayed percentages. Neither summary replaces the other: the pooled rate describes combined results, while the suite rates show variation across these groups.
Why calibration limits and held-out false alarms are different
The post distinguishes a threshold-selection budget enforced using benign scores on a calibration split from false alarms measured on held-out suites. The first constrains how a threshold is selected using calibration data. The second records observed behavior on held-out data. A calibration-time constraint does not, on its own, establish that the same false-alarm rate will occur on held-out suites.
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These are separate quantities, and neither should be substituted for the other when describing results. Readers need to know which data were used for threshold selection and which data were used to measure held-out behavior.
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What to look for in an evaluation report
- Read the aggregate with its numerator and denominator. Here, 35% means 219 caught out of 629 attacks, not a rate measured on every possible deployment.
- Check each group’s count and rate. A percentage without its denominator conceals how many examples it represents.
- Identify the data behind each metric. Calibration-set constraints and held-out measurements answer different questions.
- Keep the scope narrow. The available excerpt does not establish that these four suites represent other domains or deployments, nor does it provide enough methodological detail to infer statistical significance, confidence intervals, or independent replication.
The source refers to the groups as held-out suites. Its indexed excerpt does not establish that “folds” denotes a particular cross-validation design, so the figures are best described as suite-level results.
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