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Titlebook: Blockchain for Cybersecurity in Cyber-Physical Systems; Yassine Maleh,Mamoun Alazab,Imed Romdhani Book 2023 The Editor(s) (if applicable)

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樓主: Opulent
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發(fā)表于 2025-3-23 13:40:33 | 只看該作者
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發(fā)表于 2025-3-23 17:49:06 | 只看該作者
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發(fā)表于 2025-3-24 01:37:33 | 只看該作者
Trust Management in Cyber-Physical Systems: Issues and Challenges,ed. While focusing on their security, we list the various attacks threatening Cyber-Physical Systems and focus on Trust Management as an approach to secure Cyber-Physical Systems collaborations and interactions.
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發(fā)表于 2025-3-24 05:59:31 | 只看該作者
Blockchain Technology-Based Smart Cities: A Privacy-Preservation Review,erature in the context of blockchain based-smart cities, and how they managed the privacy-preserving issue of the users, providing informative data and a reference point to urban researchers and practitioners alike.
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發(fā)表于 2025-3-24 09:42:57 | 只看該作者
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發(fā)表于 2025-3-24 12:04:43 | 只看該作者
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發(fā)表于 2025-3-24 15:01:01 | 只看該作者
AI and Blockchain for Cybersecurity in Cyber-Physical Systems: Challenges and Future Research Agend 2021 that addressed federated learning, deep learning, machine learning, graph-based approaches, intrusion detection tree approaches, Blockchain-based security for Cyber-physical systems, and Signature Based Malicious Behavior Detection in cyber-security..Additionally, the work compared various att
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發(fā)表于 2025-3-24 21:59:14 | 只看該作者
Assessing the Predictability of Bitcoin Using AI and Statistical Models,ious external sources, forecasting time series problems is intrinsically challenging. When attempting to predict stated sort of data, the following constraints must be considered: the models (1) do not account for exogenous variable uncertainty, and (2) do not account for the fact that forecast-erro
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發(fā)表于 2025-3-25 02:18:17 | 只看該作者
Proactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain,eters: sender ID, receiver ID, transaction amount, sender’s balance, and receiver’s balance. Output is either 0 (not selected into the chain) or 1 (selected into the chain) once trained neurons (each input parameter) are tested for it’s sensitivity to the selection. If it exceeds a threshold value,
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