Anomalous Anomaly Detector Detection
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Anomalous Anomaly Detector Detection
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH22_008%2F0004583" target="_blank" >EH22_008/0004583: Research of Excellence on Digital Technologies and Wellbeing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
New Trends in Intelligent Software Methodologies, Tools and Techniques
ISBN
978-1-64368-619-6
ISSN
0922-6389
e-ISSN
1879-8314
Number of pages
14
Pages from-to
625-638
Publisher name
IOS press
Place of publication
Amsterdam
Event location
Kitakyushu
Event date
Sep 23, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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