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© 2019

Deep Learning Applications for Cyber Security

  • Mamoun Alazab
  • MingJian Tang
  • 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

Book

Table of contents

  1. Front Matter
    Pages i-xx
  2. Mahmoud Nabil, Muhammad Ismail, Mohamed Mahmoud, Mostafa Shahin, Khalid Qaraqe, Erchin Serpedin
    Pages 73-102
  3. Kyle Millar, Adriel Cheng, Hong Gunn Chew, Cheng-Chew Lim
    Pages 103-126
  4. R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, Mamoun Alazab, Alireza Jolfaei
    Pages 127-149
  5. Amara Dinesh Kumar, Harish Thodupunoori, R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, Mamoun Alazab et al.
    Pages 151-173
  6. Tuan Anh Tang, Des McLernon, Lotfi Mhamdi, Syed Ali Raza Zaidi, Mounir Ghogho
    Pages 175-195
  7. Mofakharul Islam, Abdun Nur Mahmood, Paul Watters, Mamoun Alazab
    Pages 211-219
  8. Mofakharul Islam, Paul Watters, Abdun Nur Mahmood, Mamoun Alazab
    Pages 221-246

About this book

Introduction

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. 


Keywords

Big data Cyber crime Intrusion Detection Malware Analysis Image and Video detection Phishing Emails and Spams Detection Threat Intelligence and Cyber Criminology Adversarial Neural Networks Adversarial Machine Learning Deep neural networks Machine learning and cybersecurity

Editors and affiliations

  • Mamoun Alazab
    • 1
  • MingJian Tang
    • 2
  1. 1.Charles Darwin UniversityCasuarinaAustralia
  2. 2.Singtel OptusSydneyAustralia

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

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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)