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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. 

Deep Learning Applications for Cyber Security

Adversarial Attack, Defense, and Applications with Deep Learning Frameworks; Z. Yin et al.-

Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft; Y.J. Mendis et al.-

Deep Learning in Person Re-identication for Cyber-Physical Surveillance Systems; L. Wu et al.-

Deep Learning-based Detection of Electricity Theft Cyber-attacks in Smart Grid AMI Networks; M. Nabil et al.-

Using Convolutional Neural Networks for Classifying Malicious Network Traffic; K. Millar et al.-

DBD: Deep Learning DGA-based Botnet Detection; R. Vinayakumar et al.-

Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks; A.D. Kumar et al.-

Intrusion Detection in SDN-based Networks: Deep Recurrent Neural Network Approach; T.A. Tang et al.-

SeqDroid: Obfuscated Android Malware Detection using Stacked Convolutional and Recurrent Neural Networks; W. Younghoo Lee et al.-

Forensic Detection of Child Exploitation Material using Deep Learning; M. Islam et al.-

Toward Detection of Child Exploitation Material:  A Forensic Approach; M. Islam et al.