Optimizing Multi-class Classification of Binaries Based on Static Features
Classification of binaries is often done with limited resources spent on pre-processing the input, assuming that the resource-intensive machine learning techniques will find the optimal results. In this paper, we identify pre-processing methods to perform
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are Analysis Using Artificial Intelligence and Deep Learning
Malware Analysis Using Artificial Intelligence and Deep Learning
Mark Stamp Mamoun Alazab Andrii Shalaginov •
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Editors
Malware Analysis Using Artificial Intelligence and Deep Learning
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Editors Mark Stamp Department of Computer Science San Jose State University San Jose, CA, USA
Mamoun Alazab College of Engineering, IT & Environment Charles Darwin University Darwin, NT, Australia
Andrii Shalaginov Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik, Norway
ISBN 978-3-030-62581-8 ISBN 978-3-030-62582-5 https://doi.org/10.1007/978-3-030-62582-5
(eBook)
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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
Artificial intelligence (AI) is changing the world as we know it. From its humble beginnings in the late 1940s as little more than an academic curiosity, AI has gone through multiple boom and bust cycles. With recent advances in machine learning (ML) and deep learning (DL), AI has finally taken root as a fundamental transformative technology. The changes wrought by AI already affect virtually every aspect of daily life, yet we are clearly only in the early stages of an AI-based revolution. In the field of information security, there is no topic that is more significant than malware. The sheer volume of malware and the cost of dealing with its consequences are truly staggering. It is therefore timely to consider ML, DL, and AI in the context of malware analysis. The chapters in this book apply numerous cutting-edge AI technique
