Deep Learning Applications for Cyber Security
Springer International Publishing (Verlag)
978-3-030-13056-5 (ISBN)
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.
Mamoun Alazab is a Senior Lecture at the Australian National University (ANU). He is a Cyber Security researcher and practitioner with industry and academic experience. Dr. Alazab's research is multidisciplinary and includes both technological and criminological perspectives of computer crime, with a focus on crime detection and prevention. He has more than 70 publications. He works as a Lecturer in Cyber Security at Macquarie University. Based on his nomination from the Australian Academy of Science, he was awarded a fellowship from Japan Society for the Promotion of Science (JSPS). He is a Senior Member of the IEEE, Cybersecurity Academic Ambassador for Oman's Information Technology Authority (ITA), and has worked closely with government and industry on many projects, including IBM, Trend Mirco, UNODC, the Australian Federal Police, the Australian Communications and Media Authority, Westpac, and the Attorney General's Department. MingJian Tang received the PhD (with distinction) degree in computer science from La Trobe University, Melbourne, Australia, in 2009 and is currently undertaking the master of actuarial studies from the University of New South Wales, Sydney, Australia. He is currently a cyber security data scientist at one of the major Australian banks. 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.
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.
"Deep learning applications for cyber security addresses interdisciplinary topics that make deep learning a tool of major interest for cybersecurity. ... This is why the book is recommended for researchers and students, as well as for all those interested in applying deep learning as part of cybersecurity products or platforms." (Eugen Petac, Computing Reviews, May 7, 2021)
| Erscheinungsdatum | 01.09.2019 |
|---|---|
| Reihe/Serie | Advanced Sciences and Technologies for Security Applications |
| Zusatzinfo | XX, 246 p. 78 illus., 54 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 573 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Recht / Steuern ► Strafrecht ► Kriminologie | |
| Schlagworte | Adversarial Machine Learning • Adversarial Neural Networks • Big Data • cyber crime • deep neural networks • Image and Video detection • Intrusion Detection Malware Analysis • Machine learning and cybersecurity • Phishing Emails and Spams Detection • Threat Intelligence and Cyber Criminology |
| ISBN-10 | 3-030-13056-8 / 3030130568 |
| ISBN-13 | 978-3-030-13056-5 / 9783030130565 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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