Deployable Machine Learning for Security Defense

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

  • Gang Wang
  • Arridhana Ciptadi
  • Ali Ahmadzadeh
Conference proceedings MLHat 2020

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

Table of contents

  1. Front Matter
    Pages i-vii
  2. Understanding the Adversaries

    1. Front Matter
      Pages 1-1
    2. Neil Shah, Grant Ho, Marco Schweighauser, Mohamed Ibrahim, Asaf Cidon, David Wagner
      Pages 3-27
    3. Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
      Pages 28-44
  3. Adversarial ML for Better Security

    1. Front Matter
      Pages 45-45
    2. Matthew Butler, Yi Fan, Christos Faloutsos
      Pages 47-65
  4. Threats on Networks

    1. Front Matter
      Pages 103-103
    2. W. Graham Mueller, Alex Memory, Kyle Bartrem
      Pages 122-137
    3. Sowmya Myneni, Ankur Chowdhary, Abdulhakim Sabur, Sailik Sengupta, Garima Agrawal, Dijiang Huang et al.
      Pages 138-163
  5. Back Matter
    Pages 165-165

About these proceedings


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.


artificial intelligence computer crime computer security computer systems cryptography cyber-attacks data security information systems intrusion detection machine learning malware network protocols network security operating systems signal processing

Editors and affiliations

  1. 1.University of Illinois at Urbana ChampaignUrbanaUSA
  2. 2.Blue Hexagon Inc.SunnyvaleUSA
  3. 3.Blue Hexagon Inc.SunnyvaleUSA

Bibliographic information