Deep Learning Applications for Cyber Security 1st edition by Mamoun Alazab, MingJian Tang ISBN 3030130568 9783030130565
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Authors:Mamoun Alazab; Mingjian Tang , Series:Cyber Security [114] , Tags:Computers; Database Administration & Management; Social Science; Criminology; Mathematics; Applied; Security; General; Technology & Engineering; Engineering (General); Networking; Fire Science , Author sort:Alazab, Mamoun & Tang, Mingjian , Ids:Google; 9783030130572 , Languages:Languages:eng , Published:Published:Aug 2019 , Publisher:Springer , Comments:Comments: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.
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Product details:
ISBN 10: 3030130568
ISBN 13: 978-3030130565
Author: Mamoun Alazab, MingJian Tang
Deep Learning Applications for Cyber Security 1st Table of contents:
- Adversarial Attack, Defense, and Applications with Deep Learning Frameworks
- Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft
- Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems
- Deep Learning-based Detection of Electricity Theft Cyber-attacks in Smart Grid AMI Networks
- Using Convolutional Neural Networks for Classifying Malicious Network Traffic
- DBD: Deep Learning DGA-based Botnet Detection
- Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks
- Intrusion Detection in SDN-based Networks: Deep Recurrent Neural Network Approach
- SeqDroid: Obfuscated Android Malware Detection using Stacked Convolutional and Recurrent Neural Networks
- Forensic Detection of Child Exploitation Material using Deep Learning
- Toward Detection of Child Exploitation Material: A Forensic Approach
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