Deep Learning Applications for Cyber Security / Advanced Sciences and Technologies for Security Applications (PDF)
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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.
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.
- 2019, 1st ed. 2019, 246 Seiten, Englisch
- Herausgegeben: Mamoun Alazab, MingJian Tang
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030130576
- ISBN-13: 9783030130572
- Erscheinungsdatum: 14.08.2019
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- Größe: 6.45 MB
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