Skip to main content

Deployable Machine Learning for Security Defense

First International Workshop, MLHat 2020, San Diego, CA, USA, August 24, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Part of the book series: Communications in Computer and Information Science (CCIS, volume 1271)

Included in the following conference series:

Conference proceedings info: MLHat 2020.

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 papers)

  1. Understanding the Adversaries

  2. Adversarial ML for Better Security

  3. Threats on Networks

Other volumes

  1. Deployable Machine Learning for Security Defense

Keywords

About this book

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.

Editors and Affiliations

  • University of Illinois at Urbana Champaign, Urbana, USA

    Gang Wang

  • Blue Hexagon Inc., Sunnyvale, USA

    Arridhana Ciptadi, Ali Ahmadzadeh

Bibliographic Information

Publish with us