Overview
Cutting-edge technology and tools for developing secure, safe and reliable, machine learning-enabled software systems
Techniques to address different aspects of adversarial machine learning through covering diverse topics including game-playing AI, deception in AI, generative adversarial network (GAN), big data, network security, and human machine teaming
Written by eminent researchers from premier US universities and US federal research laboratories
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Table of contents(10 chapters)
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Game-Playing AI and Game Theory-Based Techniques for Cyber Defenses
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Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks
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Human Machine Interactions and Roles in Automated Cyber Defenses
About this book
Editors and Affiliations
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Information Management and Decision Architectures Branch, Information Technology Division U. S. Naval Research Laboratory, Washington, DC, USA
Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu
About the editors
Joseph B. Collins heads the Intelligent Distributed Systems Section in the Information Management & Decision Architectures Branch of the Information Technology Division at the Naval Research Laboratory (NRL). He received his Ph.D. in Physics from Brown University and has worked at NRL for over 30 years where he has investigated, designed and developed intelligent decision support systems as components of Navy simulation, command and control, and test and evaluation architectures. Over his career he has authored a variety of papers, conference publications, and book chapters. A recurring theme in his work for the Navy is the integration of sensor data and other information with analytical and physics-based models to arrive at intelligent decisions.
Ranjeev Mittu is the Branch Head for the Information Management and Decision Architectures Branch within the Information Technology Division at the U.S. Naval Research Laboratory. Mr. Mittu leads a multidisciplinary group of scientists and engineers that conduct research in visual analytics, human performance assessment, decision support systems, and enterprise systems development. His research expertise is in multi-agent systems, artificial intelligence, machine learning, data mining, and pattern recognition and anomaly detection. He has a track record for transitioning technology solutions to the operational community, and received a technology transfer award at NRL in August 2012 for his contributions to USTRANSCOM. He has authored one book, coedited five books, and written numerous book chapters and conference publications and received an MS in Electrical Engineering from The Johns Hopkins University. He is currently participating in (1) The Technical Cooperation Program (TTCP) which promotes scientific exchange between New Zealand, UK, Australia, Canada and USA; (2) the NATO Information Systems Technology Panel; and (3) the DoD Reliance 21 C4I Community of Interest. He has previously served as a Subject Matter Expert for the Joint IED Defeat Organization (2007-2008), participated as a member of the Netcentric Systems Test working group in collaboration with the U.S. Army Program Executive Office for Simulation, Training, and Instrumentation (PEO STRI), and served on NRL’s Invention Evaluation Board (IEB) to evaluate technologies and concepts for potential filing with the USPTO (2006-2008).
Bibliographic Information
Book Title: Adversary-Aware Learning Techniques and Trends in Cybersecurity
Editors: Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu
DOI: https://doi.org/10.1007/978-3-030-55692-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021
Hardcover ISBN: 978-3-030-55691-4Published: 23 January 2021
Softcover ISBN: 978-3-030-55694-5Published: 23 January 2022
eBook ISBN: 978-3-030-55692-1Published: 22 January 2021
Edition Number: 1
Number of Pages: X, 227
Number of Illustrations: 18 b/w illustrations, 50 illustrations in colour
Topics: Artificial Intelligence, Security