Deployable Machine Learning for Security Defense [electronic resource] : First International Workshop, MLHat 2020, San Diego, CA, USA, August 24, 2020, Proceedings /

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Material type: Text

TextSeries: Communications in Computer and Information Science ; 1271Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020Description: VII, 165 p. 170 illus., 45 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783030596217

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Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

004 23

LOC classification:

QA75.5-76.95

Online resources:

Click here to access online

Contents:

Understanding the Adversaries -- Adversarial ML for Better Security -- Threats on Networks.

In: Springer Nature eBookSummary: This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks.

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Understanding the Adversaries -- Adversarial ML for Better Security -- Threats on Networks.

This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks.

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