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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Mohamed Abdel-Basset,Nour Moustafa,Weiping DingPresents 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

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Studies in Computational Intelligencehttp://image.papertrans.cn/d/image/264582.jpg

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https://doi.org/10.1007/978-3-030-89025-4Cyber Forensics; Digital Forensic Education; Digital Forensics; Virtual Machines Forensics; Social Media

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978-3-030-89027-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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https://doi.org/10.1007/978-3-540-88445-3As IoT technology becomes an integral part of everyday life, enhancing productivity for businesses through automation, it should come as no surprise that attackers would seek to exploit these systems and the services they provide for profit.

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Design of Sustainable Product Life CyclesThis chapter elaborates on the potential of unsupervised deep learning solutions for assuring the security of IoT-based systems to give the reader an insightful discussion of how these solutions could satisfy the IoT security requirements necessary to realize a reliable and trustworthy IoT environment.

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https://doi.org/10.1007/978-981-10-0269-4The central intention of this chapter is to discuss the primary security challenges in internet of things (IoT) environments with the main emphasis on the opportunities for deep learning for securing and maintaining the privacy of IoT-based systems.