Anomalous Anomaly Detector Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022843" target="_blank" >RIV/62690094:18470/25:50022843 - isvavai.cz</a>

Výsledek na webu

<a href="https://ebooks.iospress.nl/doi/10.3233/FAIA250559" target="_blank" >https://ebooks.iospress.nl/doi/10.3233/FAIA250559</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA250559" target="_blank" >10.3233/FAIA250559</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anomalous Anomaly Detector Detection

Popis výsledku v původním jazyce

Anomaly detection (AD) is a technique to detect abnormal samples. However, there is a risk that attackers create anomalous AD models. The risk will be higher in federated anomaly detection, where the privacy of training data is protected. To address such a risk, this paper conceptualizes a novel problem, namely anomalous anomaly detector detection (AADD). The idea is to apply AD to AD models. For this purpose, we represent AD models as rankings of normal scores. The hypothesis is that anomalous AD models create different rankings than normal ones. Accordingly, one can transform AADD into an anomalous ranking detection problem. This study combines the nearest neighbor and ranking correlations to detect anomalous rankings. The experiment is conducted with five binary classifications and four one-class classification algorithms. The proposed method can classify AD algorithms with 100% accuracy when all AD models learn the same class. Future work includes feature extraction from AD models and detecting anomalous AADD models. © 2025 The Authors.

Název v anglickém jazyce

Anomalous Anomaly Detector Detection

Popis výsledku anglicky

Anomaly detection (AD) is a technique to detect abnormal samples. However, there is a risk that attackers create anomalous AD models. The risk will be higher in federated anomaly detection, where the privacy of training data is protected. To address such a risk, this paper conceptualizes a novel problem, namely anomalous anomaly detector detection (AADD). The idea is to apply AD to AD models. For this purpose, we represent AD models as rankings of normal scores. The hypothesis is that anomalous AD models create different rankings than normal ones. Accordingly, one can transform AADD into an anomalous ranking detection problem. This study combines the nearest neighbor and ranking correlations to detect anomalous rankings. The experiment is conducted with five binary classifications and four one-class classification algorithms. The proposed method can classify AD algorithms with 100% accuracy when all AD models learn the same class. Future work includes feature extraction from AD models and detecting anomalous AADD models. © 2025 The Authors.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH22_008%2F0004583" target="_blank" >EH22_008/0004583: Excelentní výzkum v oblasti digitálních technologií a wellbeingu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

New Trends in Intelligent Software Methodologies, Tools and Techniques

ISBN

978-1-64368-619-6

ISSN

0922-6389

e-ISSN

1879-8314

Počet stran výsledku

14

Strana od-do

625-638

Název nakladatele

IOS press

Místo vydání

Amsterdam

Místo konání akce

Kitakyushu

Datum konání akce

23. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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