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Titlebook: Deep Learning Techniques for IoT Security and Privacy; Mohamed Abdel-Basset,Nour Moustafa,Weiping Ding Book 2022 The Editor(s) (if applica

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發(fā)表于 2025-3-21 17:04:54 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Deep Learning Techniques for IoT Security and Privacy
編輯Mohamed Abdel-Basset,Nour Moustafa,Weiping Ding
視頻videohttp://file.papertrans.cn/265/264582/264582.mp4
概述Presents a Machine Learning Approach to Conducting Digital Forensics.Contains state-of-the-art research and shows how to teach hands-on incident response and digital forensic courses.Covers the applic
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Deep Learning Techniques for IoT Security and Privacy;  Mohamed Abdel-Basset,Nour Moustafa,Weiping Ding Book 2022 The Editor(s) (if applica
描述.This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book‘s material..
出版日期Book 2022
關鍵詞Cyber Forensics; Digital Forensic Education; Digital Forensics; Virtual Machines Forensics; Social Media
版次1
doihttps://doi.org/10.1007/978-3-030-89025-4
isbn_softcover978-3-030-89027-8
isbn_ebook978-3-030-89025-4Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Philosophy of Hull Structure Designibutes of IoT systems that might possibly threaten the security of the system. Firstly, the definition and of the IoT system and the detailed description of its architecture are presented along with a taxonomy for dividing its architecture into layers with different complementary roles. Secondly, th
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Application of the VENUS Design System, determinations throughout time, which is a popular and broad challenge explored in lots of technical and industrial disciplines. RL nativey integrates an additional dimension (which is typically time, however not of necessity) into learning process, which lays it considerably near to the social awa
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https://doi.org/10.1007/978-981-10-0269-4ities are thought to come up with multiple crucial smart IoT applications i.e., smart manufacturing, smart transportation, autonomous driving/flight, smart buildings, smart healthcare, smart grid, and so many others (Lo et al. in ACM Comput. Surv., 2021). Effective deployment of these IoT applicatio
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Deep Reinforcement Learning for Secure Internet of Things, determinations throughout time, which is a popular and broad challenge explored in lots of technical and industrial disciplines. RL nativey integrates an additional dimension (which is typically time, however not of necessity) into learning process, which lays it considerably near to the social awareness of AI.
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