Deep Learning Applications for Cyber Security

Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensi

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Mamoun Alazab MingJian Tang Editors

Deep Learning Applications for Cyber Security

Advanced Sciences and Technologies for Security Applications Series editor Anthony J. Masys, Associate Professor, Director of Global Disaster Management, Humanitarian Assistance and Homeland Security, University of South Florida, Tampa, USA Advisory Board Gisela Bichler, California State University, San Bernardino, CA, USA Thirimachos Bourlai, West Virginia University, Morgantown, WV, USA Chris Johnson, University of Glasgow, Glasgow, UK Panagiotis Karampelas, Hellenic Air Force Academy, Attica, Greece Christian Leuprecht, Royal Military College of Canada, Kingston, ON, Canada Edward C. Morse, University of California, Berkeley, CA, USA David Skillicorn, Queen’s University, Kingston, ON, Canada Yoshiki Yamagata, National Institute for Environmental Studies, Tsukuba, Japan

The series Advanced Sciences and Technologies for Security Applications comprises interdisciplinary research covering the theory, foundations and domain-specific topics pertaining to security. Publications within the series are peer-reviewed monographs and edited works in the areas of: − biological and chemical threat recognition and detection (e.g., biosensors, aerosols, forensics) − crisis and disaster management − terrorism − cyber security and secure information systems (e.g., encryption, optical and photonic systems) − traditional and non-traditional security − energy, food and resource security − economic security and securitization (including associated infrastructures) − transnational crime − human security and health security − social, political and psychological aspects of security − recognition and identification (e.g., optical imaging, biometrics, authentication and verification) − smart surveillance systems − applications of theoretical frameworks and methodologies (e.g., grounded theory, complexity, network sciences, modelling and simulation) Together, the high-quality contributions to this series provide a cross-disciplinary overview of forefront research endeavours aiming to make the world a safer place. The editors encourage prospective authors to correspond with them in advance of submitting a manuscript. Submission of manuscripts should be made to the Editor-in-Chief or one of the Editors.

More information about this series at http://www.springer.com/series/5540

Mamoun Alazab • MingJian Tang Editors

Deep Learning Applications for Cyber Security

123

Editors Mamoun Alazab Charles Darwin University Casuarina, NT, Australia

MingJian Tang Singtel Optus Sydney, NSW, Australia

ISSN 1613-5113 ISSN 2363-9466 (electronic) Advanced Sciences and Technologies for Security Applications ISBN 978-3-030-13056-5 ISBN 978-3-030-13057-2 (eBook) https://doi.org/10.1007/978-3-030-13057-2 © Springer Nature Switzerland AG 2019 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,

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