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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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樓主: Concave
11#
發(fā)表于 2025-3-23 10:34:46 | 只看該作者
12#
發(fā)表于 2025-3-23 16:28:48 | 只看該作者
Artificial Intelligence and Security of Industrial Control Systems,med in order to accelerate or slow down ICS operations post such specialized attacks. This may result in total industrial equipment being destroyed or permanently damaged, or also may endanger personnel’s life who is working at the ICS site. This chapter surveys about the latest research and methodo
13#
發(fā)表于 2025-3-23 18:53:56 | 只看該作者
14#
發(fā)表于 2025-3-23 23:40:07 | 只看該作者
Big Data Application for Security of Renewable Energy Resources,ent aspects of security are summarized in the same context while RES is specifically addressed. Next, we introduce big data and finally, a relation between big data and security aspect of using RES is discussed.
15#
發(fā)表于 2025-3-24 04:12:00 | 只看該作者
A Survey on Application of Big Data in Fin Tech Banking Security and Privacy,rity program that should be used to achieve this goal, the types of crime and cyber wars are known and adopted strategies to overcome them in FinTech-banking as big data media. Big data, both in the real world and in cyberspace, is the most significant challenges in managing large volumes of informa
16#
發(fā)表于 2025-3-24 09:43:22 | 只看該作者
A Hybrid Deep Generative Local Metric Learning Method for Intrusion Detection,ster 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).
17#
發(fā)表于 2025-3-24 13:41:13 | 只看該作者
Malware Elimination Impact on Dynamic Analysis: An Experimental Machine Learning Approach,d in this regard eliminate many of the samples for such reasons as the corruption of the file or lack of proper running as well as some other reasons. This makes the results be unreliable in the real world because it is possible to infect the system by a malware like omitted instances. This chapter
18#
發(fā)表于 2025-3-24 16:17:29 | 只看該作者
19#
發(fā)表于 2025-3-24 22:43:12 | 只看該作者
Active Spectral Botnet Detection Based on Eigenvalue Weighting,ve approach. Next, we propose a new method that leverages a combination of eigenvalues and eigenvectors. Furthermore, a new metric is introduced to compare active spectral algorithms by considering the directions of most important eigenvectors of queried matrix related to a complete matrix.
20#
發(fā)表于 2025-3-25 01:32:59 | 只看該作者
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