Entropy-Based Feature Grouping in Machine Learning for Android Malware Classification
In this paper, we have developed a tool to perform an analysis for all APIs over an APK and all APIs of every version of Android, to solve problems of overfitting in machine-learning-based malware classification. The tool is Java-based software consisting
- PDF / 35,361,258 Bytes
- 420 Pages / 439.37 x 666.142 pts Page_size
- 45 Downloads / 196 Views
Ilsun You (Ed.)
Information Security Applications 21st International Conference, WISA 2020 Jeju Island, South Korea, August 26–28, 2020 Revised Selected Papers
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
12583
More information about this subseries at http://www.springer.com/series/7410
Ilsun You (Ed.)
Information Security Applications 21st International Conference, WISA 2020 Jeju Island, South Korea, August 26–28, 2020 Revised Selected Papers
123
Editor Ilsun You Soonchunhyang University Asan, Korea (Republic of)
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-65298-2 ISBN 978-3-030-65299-9 (eBook) https://doi.org/10.1007/978-3-030-65299-9 LNCS Sublibrary: SL4 – Security and Cryptology © Springer Nature Switzerland AG 2020 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 publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Over the past decades, many advances in information technologies that include artificial intelligence (AI), 5G, blockchain, Internet of Things (IoT), and many more provided beneficial effects on various aspects of our lives. However, these advancements are accompanied with even more sophisticated threats to individuals, businesses, and government’s most valuable data assets. Cybercriminals are also exploiting such technologies to find vulnerabilities and develop more so
