Deep learning applications for cyber security / Mamoun Alazeb, MingJian Tang, editors.
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 questio...
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| Other Authors: | , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2019]
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| Series: | Advanced sciences and technologies for security applications.
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| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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. |
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| Item Description: | 6.3 Generalized Detector Evaluation with Genetic-Based Search for Hyper-Parameters Tuning. |
| Physical Description: | 1 online resource (260 pages). |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 3030130568 9783030130565 3030130576 9783030130572 |
| ISSN: | 2363-9466 |