Adaptive approach for density-approximating neural network models for anomaly detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00344025" target="_blank" >RIV/68407700:21340/21:00344025 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-57805-3_39" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-57805-3_39</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57805-3_39" target="_blank" >10.1007/978-3-030-57805-3_39</a>

Result language

angličtina

Original language name

Adaptive approach for density-approximating neural network models for anomaly detection

Original language description

We propose an adaptive approach for density-approximating neural network models, the alternative use of neural models in anomaly detection. Instead of modeling anomaly indirectly through reconstruction error as is common in auto-encoders, we propose to use a neural model to efficiently approximate anomaly as inferred by k-Nearest Neighbor, which is popular due to its good performance as anomaly detector. We propose an adaptive approach to model the space of kNN inferred anomalies to obtain a neural model with comparable accuracy and considerably better time and space complexity. Moreover, the neural model can achieve even better accuracy in case of noisy data as it allows better control of over-fitting through control of its expressivity. The key contribution over our previous results is the adaptive coverage of kNN induced anomaly space through modified Parzen estimate, which then enables generating arbitrarily large training set for neural model training. We evaluate the proposed approach on real-world computer network traffic data provided by Cisco Systems.

Czech name

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Czech description

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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)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Article name in the collection

Advances in Intelligent Systems and Computing

ISBN

9783030578046

ISSN

2194-5357

e-ISSN

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Number of pages

11

Pages from-to

415-425

Publisher name

Springer Nature

Place of publication

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Event location

Burgos

Event date

Sep 16, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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