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Deep Learning Applications for Cyber Security

Overview of attention for book
Cover of 'Deep Learning Applications for Cyber Security'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Adversarial Attack, Defense, and Applications with Deep Learning Frameworks
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    Chapter 2 Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft
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    Chapter 3 Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems
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    Chapter 4 Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks
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    Chapter 5 Using Convolutional Neural Networks for Classifying Malicious Network Traffic
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    Chapter 6 DBD: Deep Learning DGA-Based Botnet Detection
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    Chapter 7 Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks
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    Chapter 8 Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach
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    Chapter 9 SeqDroid: Obfuscated Android Malware Detection Using Stacked Convolutional and Recurrent Neural Networks
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    Chapter 10 Forensic Detection of Child Exploitation Material Using Deep Learning
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    Chapter 11 Toward Detection of Child Exploitation Material: A Forensic Approach
Attention for Chapter 6: DBD: Deep Learning DGA-Based Botnet Detection
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Citations

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33 Mendeley
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Chapter title
DBD: Deep Learning DGA-Based Botnet Detection
Chapter number 6
Book title
Deep Learning Applications for Cyber Security
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-13057-2_6
Book ISBNs
978-3-03-013056-5, 978-3-03-013057-2
Authors

R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, Mamoun Alazab, Alireza Jolfaei, Vinayakumar, R., Soman, K. P., Poornachandran, Prabaharan, Alazab, Mamoun, Jolfaei, Alireza

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 15%
Other 3 9%
Lecturer 3 9%
Researcher 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 15 45%
Readers by discipline Count As %
Computer Science 10 30%
Engineering 5 15%
Social Sciences 1 3%
Philosophy 1 3%
Unknown 16 48%

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