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Titlebook: Handbook of Big Data Privacy; Kim-Kwang Raymond Choo,Ali Dehghantanha Book 2020 Springer Nature Switzerland AG 2020 cyber threat.cyber sec

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發(fā)表于 2025-3-21 19:11:07 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Handbook of Big Data Privacy
編輯Kim-Kwang Raymond Choo,Ali Dehghantanha
視頻videohttp://file.papertrans.cn/421/420872/420872.mp4
概述One of the first handbooks that provides interdisciplinary coverage of security, privacy and forensics knowledge in the field of big data and IoT security, privacy, and forensics.Presents an up-to-dat
圖書封面Titlebook: Handbook of Big Data Privacy;  Kim-Kwang Raymond Choo,Ali Dehghantanha Book 2020 Springer Nature Switzerland AG 2020 cyber threat.cyber sec
描述This handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook.?.The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks.?
出版日期Book 2020
關鍵詞cyber threat; cyber security; privacy; big data; threat intelligence; machine learning; cyber forensics; in
版次1
doihttps://doi.org/10.1007/978-3-030-38557-6
isbn_softcover978-3-030-38559-0
isbn_ebook978-3-030-38557-6
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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https://doi.org/10.1007/978-3-662-10479-8ster to learn local metrics within each cluster to apply a robust nearest neighbor classifier. The empirical results on NSL-KDD dataset demonstrate that our model outperforms previous models designed to detect two major harmful attacks (U2R and R2L).
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network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks.?978-3-030-38559-0978-3-030-38557-6
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