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Deployable Machine Learning for Security Defense

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Deployable Machine Learning for Security Defense Synopsis

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

About This Edition

ISBN: 9783030596200
Publication date:
Author: Gang Wang
Publisher: Springer Nature Switzerland AG
Format: Paperback
Pagination: 165 pages
Series: Communications in Computer and Information Science
Genres: Information technology: general topics
Computer security
Network security
Computer fraud and hacking
Artificial intelligence
Network hardware
Applied computing

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