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arXiv · 2610.35235

False-Alarm Improvements and Detection Trade-Offs in QML Intrusion Detection: A Secondary Analysis of the Kaissar Corpus

Roberto Fernández-Barrios·Iker Pastor-López·Asier González-Santocildes·Pablo García Bringas
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Abstract

Published results in quantum machine learning for intrusion detection (QML-IDS) require joint interpretation of false-positive rate (FPR), attack true-positive rate (TPR), and evaluation provenance. We reconstruct operating points in the 25-entry frame selected by Kaissar and colleagues: 21 included articles, 20 identified documentary families, substantive methodological material consulted for 15, and 24 descriptive points from five. In one test-labelled comparison, a quantum support vector machine reduces FPR from 6.875% for random forest to 3.329%, while TPR decreases from 93.955% to 93.682%; both rates improve against three other comparators. In another study, a complete benign row identifies FPR as 12/6000 (0.200%), despite incomplete attack-side allocation that requires additional assumptions for TPR. Normalized matrices identify detection ranges rather than a unique aggregate rate. A uniform provenance rule separates training-described and test-adjusted results from six conditional illustrations whose independence remains unverified. The contribution is a reproducible interpretation of favorable comparisons and detection trade-offs, without converting descriptive results or hypothetical calculations into operational guarantees.

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Roberto Fernández-Barrios, Iker Pastor-López, Asier González-Santocildes, Pablo García Bringas. 2026-10-26. False-Alarm Improvements and Detection Trade-Offs in QML Intrusion Detection: A Secondary Analysis of the Kaissar Corpus. https://arxiv.org/abs/2610.35235

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