National Academies Press: OpenBook
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

Robust MACHINE LEARNING
Algorithms and Systems for
DETECTION and MITIGATION of
Adversarial Attacks and Anomalies

PROCEEDINGS OF A WORKSHOP

Linda Casola and Dionna Ali, Rapporteurs

Intelligence Community Studies Board

Board on Mathematical Sciences and Analytics

Computer Science and Telecommunications Board

Division on Engineering and Physical Sciences

images

THE NATIONAL ACADEMIES PRESS
Washington, DC
www.nap.edu

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001

This activity was supported by Contract 2014-14041100003-019 with the Office of the Director of National Intelligence. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

International Standard Book Number-13: 978-0-309-49609-4
International Standard Book Number-10: 0-309-49609-8
Digital Object Identifier: https://doi.org/10.17226/25534

Additional copies of this publication are available for sale from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu.

Copyright 2019 by the National Academy of Sciences. All rights reserved.

Printed in the United States of America

Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/25534.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

Image

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president.

The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. John L. Anderson is president.

The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president.

The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine.

Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

Image

Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study’s statement of task by an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and the committee’s deliberations. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task.

Proceedings published by the National Academies of Sciences, Engineering, and Medicine chronicle the presentations and discussions at a workshop, symposium, or other event convened by the National Academies. The statements and opinions contained in proceedings are those of the participants and are not endorsed by other participants, the planning committee, or the National Academies.

For information about other products and activities of the National Academies, please visit www.nationalacademies.org/about/whatwedo.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

PLANNING COMMITTEE ON ENSURING THE QUALITY OF MACHINE-GENERATED ANALYTIC PRODUCTS FROM MULTI-SOURCE DATA: A WORKSHOP

RAMA CHELLAPPA, University of Maryland, College Park, Chair

TODD BORKEY, Alion Science and Technology

JULIE BRILL, Microsoft Corporation

LISE GETOOR, University of California, Santa Cruz

ANTHONY HOOGS, Kitware, Inc.

ANITA JONES, NAE,1 University of Virginia

YUNYAO LI, IBM Corporation

JOYSULA RAO, IBM Corporation

SAMUEL VISNER, MITRE Corporation

Staff

GEORGE COYLE, Senior Program Officer, Workshop Director

CHRIS JONES, Financial Officer

MARGUERITE SCHNEIDER, Administrative Coordinator

DIONNA ALI, Research Associate

NATHANIEL DEBEVOISE, Senior Program Assistant

___________________

1 Member, National Academy of Engineering.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

INTELLIGENCE COMMUNITY STUDIES BOARD

FREDERICK CHANG, NAE,1 Southern Methodist University, Co-Chair

ROBERT C. DYNES, NAS,2 University of California, San Diego, Co-Chair

JULIE BRILL, Microsoft Corporation

ROBERT A. BRODOWSKI, MITRE Corporation

TOMÁS DÍAZ DE LA RUBIA, Purdue University Discovery Park

ROBERT FEIN, McLean Hospital/Harvard Medical School

MIRIAM JOHN, Independent Consultant

ANITA JONES, NAE, University of Virginia

ROBERT H. LATIFF, R. Latiff Associates

RICHARD H. LEDGETT, JR., Institute for Defense Analyses

MARK LOWENTHAL, Johns Hopkins University

MICHAEL MARLETTA, NAS/NAM,3 University of California, Berkeley

L. ROGER MASON, JR., Peraton

JASON MATHENY, Georgetown University

CARMEN L. MIDDLETON, Consultant

ELIZABETH RINDSKOPF PARKER, State Bar of California (retired)

WILLIAM H. PRESS, NAS, University of Texas, Austin

DAVID A. RELMAN, NAM, Stanford University

SAMUEL VISNER, MITRE Corporation

Staff

ALAN SHAW, Director

CARYN LESLIE, Senior Program Officer

CHRIS JONES, Financial Manager

MARGUERITE SCHNEIDER, Administrative Coordinator

DIONNA ALI, Research Associate

NATHANIEL DEBEVOISE, Senior Program Assistant

___________________

1 Member, National Academy of Engineering.

2 Member, National Academy of Sciences.

3 Member, National Academy of Medicine.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

BOARD ON MATHEMATICAL SCIENCES AND ANALYTICS

MARK L. GREEN, University of California, Los Angeles, Chair

JOHN R. BIRGE, NAE,1 University of Chicago

HÉLÈNE BARCELO, Mathematical Sciences Research Institute

RUSSEL E. CAFLISCH, NAS,2 New York University

W. PETER CHERRY, NAE, Independent Consultant

DAVID S.C. CHU, Institute for Defense Analyses

RONALD R. COIFMAN, NAS, Yale University

JAMES (JIM) H. CURRY, University of Colorado, Boulder

SHAWNDRA HILL, Microsoft Research

LYDIA KAVRAKI, NAM,3 Rice University

TAMARA KOLDA, Sandia National Laboratories

RACHEL KUSKE, Georgia Institute of Technology

JOSEPH A. LANGSAM, University of Maryland, College Park

DAVID MAIER, Portland State University

LOIS CURFMAN MCINNES, Argonne National Laboratory

JILL PIPHER, Brown University

ELIZABETH A. THOMPSON, NAS, University of Washington

CLAIRE TOMLIN, NAE, University of California, Berkeley

LANCE WALLER, Emory University

KAREN E. WILLCOX, University of Texas, Austin

DAVID YAO, NAE, Columbia University

Staff

MICHELLE K. SCHWALBE, Director

TYLER KLOEFKORN, Program Officer

LINDA CASOLA, Associate Program Officer

ADRIANNA HARGROVE, Financial Manager

SELAM ARAIA, Program Assistant

___________________

1 Member, National Academy of Engineering.

2 Member, National Academy of Sciences.

3 Member, National Academy of Medicine.

Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

COMPUTER SCIENCE AND TELECOMMUNICATIONS BOARD

FARNAM JAHANIAN, Carnegie Mellon University, Chair

LUIZ ANDRÉ BARROSO, Google, Inc.

STEVEN M. BELLOVIN, NAE,1 Columbia University

ROBERT F. BRAMMER, Brammer Technology, LLC

DAVID CULLER, NAE, University of California, Berkeley

EDWARD FRANK, NAE, Cloud Parity, Inc.

LAURA HAAS, NAE, University of Massachusetts, Amherst

MARK HOROWITZ, NAE, Stanford University

ERIC HORVITZ, NAE, Microsoft Corporation

VIJAY KUMAR, NAE, University of Pennsylvania

BETH MYNATT, Georgia Institute of Technology

CRAIG PARTRIDGE, Colorado State University

DANIELA RUS, NAE, Massachusetts Institute of Technology

FRED B. SCHNEIDER, NAE, Cornell University

MARGO SELTZER, University of British Columbia

MOSHE VARDI, NAS2/NAE, Rice University

Staff

JON EISENBERG, Senior Director

LYNETTE I. MILLETT, Director, Forum on Cyber Resilience

RENEE HAWKINS, Financial and Administrative Manager

SHENAE BRADLEY, Administrative Assistant

KATIRIA ORTIZ, Associate Program Officer

___________________

1 Member, National Academy of Engineering.

2 Member, National Academy of Sciences.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

Acknowledgments

This Proceedings of a Workshop was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.

We thank the following individuals for their review of this proceedings:

Terrance Boult, University of Colorado, Colorado Springs,

Dianne Chong, NAE,1 Boeing Research and Technology (retired),

Anita Jones, NAE,2 University of Virginia, and

Yunyao Li, IBM Corporation.

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The review of this proceedings was overseen by Ellen W. Clayton, NAM,3 Vanderbilt University Medical Center. She was responsible for making certain that an independent examination of this proceedings was carried out in accordance with standards of the National Academies and that all review comments were carefully considered. We also wish to thank Michelle Schwalbe, National Academies, for her guidance in the drafting of this manuscript. Responsibility for the final content rests entirely with the rapporteurs and the National Academies.

___________________

1 Member, National Academy of Engineering.

2 Member, National Academy of Engineering.

3 Member, National Academy of Medicine.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×

This page intentionally left blank.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R1
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R2
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R3
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R4
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R5
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R6
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R7
Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R8
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R9
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R10
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R11
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
×
Page R12
Next: 1 Introduction »
Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop Get This Book
×
Buy Ebook | $14.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

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.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!