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Deep Learning Applications for Cyber Security

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Deep Learning Applications for Cyber Security Synopsis

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. 


About This Edition

ISBN: 9783030130596
Publication date: 30th August 2020
Author: Mamoun Alazab, MingJian Tang
Publisher: Springer an imprint of Springer International Publishing
Format: Paperback
Pagination: 246 pages
Series: Advanced Sciences and Technologies for Security Applications
Genres: Databases
Computer crime, cybercrime
Mathematical modelling
Security and fire alarm systems
Network security
Computer security