Deep Learning Applications for Cyber Security

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.

Bridges two popular areas (Deep Learning and Cyber Security) with self-contained material Fully self-contained with ample practical examples Provides wide coverage of popular Deep Learning tools and frameworks enabling the readers to quickly develop workable and advanced prototypes Combines academic excellence with extensive practical lessons

Autorentext
Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. He received his PhD degree in Computer Science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cyber security researcher and practitioner with industry and academic experience. Alazab's research is multidisciplinary that focuses on cyber security and digital forensics of computer systems with a focus on cybercrime detection and prevention. He has more than 100 research papers. He delivered many invited and keynote speeches, 22 events in 2018 alone. He convened and chaired more than 50 conferences and workshops. He works closely with government and industry on many projects. He is an editor on multiple editorial boards of international journals and a Senior Member of the IEEE.

MingJian Tang is a Senior Data Scientist at Singtel Optus, Australia. He received his PhD degree in Computer Science from La Trobe University, Melbourne, Australia, in 2009. Previously he was a Data Scientist at the Commonwealth Bank of Australia. He has participated in several industry-based research projects including unsupervised fraud detection, unstructured threat intelligence, cyber risk analysis and quantification, and big data analysis.

Inhalt
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.