Fraud Prevention in Online Digital Advertising
The authors systematically review methods of online digital advertising (ad) fraud and the techniques to prevent and defeat such fraud in this brief. The authors categorize ad fraud into three major categories, including (1) placement fraud, (2) traffic f
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Xingquan Zhu Haicheng Tao Zhiang Wu Jie Cao Kristopher Kalish Jeremy Kayne
Fraud Prevention in Online Digital Advertising
123
SpringerBriefs in Computer Science Series editors Stan Zdonik, Brown University, Providence, Rhode Island, USA Shashi Shekhar, University of Minnesota, Minneapolis, Minnesota, USA Jonathan Katz, University of Maryland, College Park, Maryland, USA Xindong Wu, University of Vermont, Burlington, Vermont, USA Lakhmi C. Jain, University of South Australia, Adelaide, South Australia, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, Illinois, USA Xuemin (Sherman) Shen, University of Waterloo, Waterloo, Ontario, Canada Borko Furht, Florida Atlantic University, Boca Raton, Florida, USA V.S. Subrahmanian, University of Maryland, College Park, Maryland, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan Bruno Siciliano, Università di Napoli Federico II, Napoli, Campania, Italy Sushil Jajodia, George Mason University, Fairfax, Virginia, USA Newton Lee, Newton Lee Laboratories, LLC, Tujunga, California, USA
More information about this series at http://www.springer.com/series/10028
Xingquan Zhu • Haicheng Tao • Zhiang Wu Jie Cao • Kristopher Kalish • Jeremy Kayne
Fraud Prevention in Online Digital Advertising
123
Xingquan Zhu Dept. of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton, FL, USA
Haicheng Tao Dept. of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton, FL, USA
Zhiang Wu College of Information Engineering Nanjing University of Finance and Economics Nanjing, China
Jie Cao College of Information Engineering Nanjing University of Finance and Economics Nanjing, China
Kristopher Kalish Bidtellect, Inc. Delray Beach, FL, USA
Jeremy Kayne Bidtellect, Inc. Delray Beach, FL, USA
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-3-319-56792-1 ISBN 978-3-319-56793-8 (eBook) DOI 10.1007/978-3-319-56793-8 Library of Congress Control Number: 2017942765 © The Author(s) 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publicat
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