Overview
- Bridges two popular areas (Deep Learning and Cyber Security) with self-contained material
- Fully self-contained with ample practical examples
- Provides wide coverage of popular Deep Learning tools and frameworks enabling the readers to quickly develop workable and advanced prototypes
- Combines academic excellence with extensive practical lessons
Part of the book series: Advanced Sciences and Technologies for Security Applications (ASTSA)
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Table of contents (11 chapters)
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Front Matter
Keywords
About this book
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 forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Reviews
“Deep learning applications for cyber security addresses interdisciplinary topics that make deep learning a tool of major interest for cybersecurity. … This is why the book is recommended for researchers and students, as well as for all those interested in applying deep learning as part of cybersecurity products or platforms.” (Eugen Petac, Computing Reviews, May 7, 2021)
Editors and Affiliations
About the editors
Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. He received his PhD degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cyber security researcher and practitioner with industry and academic experience. Alazab’s research is multidisciplinary that focuses on cyber security and digital forensics of computer systems with a focus on cybercrime detection and prevention. He has more than 100 research papers. He delivered many invited and keynote speeches, 22 events in 2018 alone. He convened and chaired more than 50 conferences and workshops. He works closely with government and industry on many projects. He is an editor on multiple editorial boards of international journals and a Senior Member of the IEEE.
MingJian Tang is a Senior Data Scientist at Singtel Optus, Australia. He received his PhD degree in Computer Science from La Trobe University, Melbourne, Australia, in 2009. Previously he was a Data Scientist at the Commonwealth Bank of Australia. He has participated in several industry-based research projects including unsupervised fraud detection, unstructured threat intelligence, cyber risk analysis and quantification, and big data analysis.
Bibliographic Information
Book Title: Deep Learning Applications for Cyber Security
Editors: Mamoun Alazab, MingJian Tang
Series Title: Advanced Sciences and Technologies for Security Applications
DOI: https://doi.org/10.1007/978-3-030-13057-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-13056-5Published: 30 August 2019
Softcover ISBN: 978-3-030-13059-6Published: 30 August 2020
eBook ISBN: 978-3-030-13057-2Published: 14 August 2019
Series ISSN: 1613-5113
Series E-ISSN: 2363-9466
Edition Number: 1
Number of Pages: XX, 246
Number of Illustrations: 24 b/w illustrations, 54 illustrations in colour
Topics: Big Data, Cybercrime, Mathematical Models of Cognitive Processes and Neural Networks, Systems and Data Security, Security Science and Technology