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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

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    Book Overview
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    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 7: Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks
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3 patents

Citations

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54 Dimensions

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11 Mendeley
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Chapter title
Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks
Chapter number 7
Book title
Deep Learning Applications for Cyber Security
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-13057-2_7
Book ISBNs
978-3-03-013056-5, 978-3-03-013057-2
Authors

Amara Dinesh Kumar, Harish Thodupunoori, R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran, Mamoun Alazab, Sitalakshmi Venkatraman

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Researcher 2 18%
Lecturer 1 9%
Unknown 5 45%
Readers by discipline Count As %
Computer Science 5 45%
Chemistry 1 9%
Engineering 1 9%
Unknown 4 36%

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