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Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Proceedings of a Workshop

  • Author: Ali, Dionna; Casola, Linda
  • Publisher: National Academies Press
  • ISBN: 9780309496094
  • eISBN Pdf: 9780309496100
  • eISBN Epub: 9780309496124
  • Place of publication:  United States
  • Year of digital publication: 2019
  • Month: August
  • Pages: 83
  • Language: English

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

  • FrontMatter
  • Acknowledgments
  • Contents
  • 1 Introduction
  • 2 Plenary Session
  • 3 Adversarial Attacks
  • 4 Detection and Mitigation of Adversarial Attacks and Anomalies
  • 5 Enablers of Machine Learning Algorithms and Systems
  • 6 Recent Trends in Machine Learning, Parts 1 and 2
  • 7 Plenary Session
  • 8 Recent Trends in Machine Learning, Part 3
  • 9 Machine Learning Systems
  • References
  • Appendixes
  • Appendix A: Biographical Sketches of Workshop Planning Committee
  • Appendix B: Workshop Agenda
  • Appendix C: Workshop Statement of Task
  • Appendix D: Capability Technology Matrix
  • Appendix E: Acronyms

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  • Loan duration: 21 days
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