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Deep learning applications for cyber...

Alazab, Mamoun.

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Deep learning applications for cyber security

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning applications for cyber security/ edited by Mamoun Alazab, MingJian Tang.
其他作者:	Alazab, Mamoun.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xx, 246 p. :ill., digital ;24 cm.
內容註:	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-identication 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.
Contained By:	Springer Nature eBook
標題:	Computer security. -
電子資源:	https://doi.org/10.1007/978-3-030-13057-2
ISBN:	9783030130572

Deep learning applications for cyber security
Deep learning applications for cyber security[electronic resource] /edited by Mamoun Alazab, MingJian Tang. - Cham :Springer International Publishing :2019. - xx, 246 p. :ill., digital ;24 cm. - Advanced sciences and technologies for security applications,1613-5113. - Advanced sciences and technologies for security applications..

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-identication 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.

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.

ISBN: 9783030130572

Standard No.: 10.1007/978-3-030-13057-2doiSubjects--Topical Terms:

540555
Computer security.

LC Class. No.: QA76.9.A25 / D44 2019

Dewey Class. No.: 005.8

Deep learning applications for cyber security
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520 $a 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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