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Publication type: Book part
Type of review: Editorial review
Title: Security of data science and data science for security
Authors: Tellenbach, Bernhard
Rennhard, Marc
Schweizer, Remo
DOI: 10.1007/978-3-030-11821-1_15
Published in: Applied data science : lessons learned for the data-driven business
Editors of the parent work: Braschler, Martin
Stadelmann, Thilo
Stockinger, Kurt
Page(s): 265
Pages to: 288
Issue Date: 2019
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-11820-4
Language: English
Subjects: Information security; Data science
Subject (DDC): 005: Computer programming, programs and data
Abstract: In this chapter, we present a brief overview of important topics regarding the connection of data science and security. In the first part, we focus on the security of data science and discuss a selection of security aspects that data scientists should consider to make their services and products more secure. In the second part about security for data science, we switch sides and present some applications where data science plays a critical role in pushing the state-of-the-art in securing information systems. This includes a detailed look at the potential and challenges of applying machine learning to the problem of detecting obfuscated JavaScripts.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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