Deep Learning Applications for Cyber Security


Deep Learning Applications for Cyber Security (Advanced Sciences and Technologies for Security Applications)
by: Mamoun Alazab
ISBN-10: 3030130568
ISBN-13: 9783030130565
Edition 版本:‏ 1st ed. 2019
Released: 2019-08-15
Pages: 246 pages
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.

Book Description


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 AMl Networks
Using Convolutional Neural Networks for Classifying Malicious Network Traffhc
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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