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Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences Read chapter Read first chapter

2019 | OriginalPaper | Chapter

The Feasibility of Deep Learning Use for Adversarial Model Extraction in the Cybersecurity Domain

Authors : Michał Choraś, Marek Pawlicki, Rafał Kozik

Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2019

Publisher: Springer International Publishing

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Abstract

Machine learning algorithms found their way into a surprisingly wide range of applications, providing utility and allowing for insights gathered from data in a way never before possible. Those tools, however, have not been developed with security in mind. A deployed algorithm can meet a multitude of risks in the real world. This work explores one of those risks - the feasibility of an exploratory attack geared towards stealing an algorithm used in the cybersecurity domain. The process we have used is thoroughly explained and the results are promising.

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previous chapter A Genetic-Based Ensemble Learning Applied to Imbalanced Data Classification
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Metadata
Title
The Feasibility of Deep Learning Use for Adversarial Model Extraction in the Cybersecurity Domain
Authors
Michał Choraś
Marek Pawlicki
Rafał Kozik
Copyright Year
2019
Book
Intelligent Data Engineering and Automated Learning – IDEAL 2019

Print ISBN: 978-3-030-33616-5

Electronic ISBN: 978-3-030-33617-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-33617-2_36

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