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Titlebook: Handbook of Big Data Analytics and Forensics; Kim-Kwang Raymond Choo,Ali Dehghantanha Book 2022 Springer Nature Switzerland AG 2022 cyber

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樓主: hearken
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發(fā)表于 2025-3-28 14:51:07 | 只看該作者
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發(fā)表于 2025-3-28 20:40:54 | 只看該作者
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發(fā)表于 2025-3-29 02:49:40 | 只看該作者
W. Schr?ter,G. Landbeck,U. G?belin the machine learning techniques. Over the years, many fair machine learning algorithms have been established to reduce the discrimination factor in machine learning. The fair variants of machine learning techniques such as fair clustering models provide a solution to the biased data analysis prob
44#
發(fā)表于 2025-3-29 06:53:57 | 只看該作者
E. W. Keck,M. Bourgeois,H. Meyer,R. Lierschusly seen data. This is most effectively accomplished by machine learning algorithms which are designed to detect abnormal activity, because a system under attack is likely to exhibit anomalous behavior. Due to the fact that anomalous behavior is not guaranteed to be caused by an attacker, false pos
45#
發(fā)表于 2025-3-29 07:41:14 | 只看該作者
Therapie der Krankheiten des Kindesalterson of the computer with traditional physical infrastructure can improve the efficiency of such facility-based systems. However, it increases the scope of attack from physical security to a cybersecurity perspective. Thus, it becomes critical for authorities of such systems to be able to identify the
46#
發(fā)表于 2025-3-29 14:43:21 | 只看該作者
https://doi.org/10.1007/978-3-662-10465-1 used, unsupervised machine learning technique to detect malware from behavior data of control systems. Clustering algorithms can be susceptible to amplifying biases that may be present in the input datasets. Recent works in fair clustering attempt to solve this problem by making them balanced with
47#
發(fā)表于 2025-3-29 16:26:41 | 只看該作者
Frühgeborene und hypotrophe Neugeborenewever, the number of attacks for Mac OS has increased exponentially over recent years and new attacks are arising daily which is capable of bypassing the Mac inbuilt security mechanism. Various supervised and unsupervised machine learning classifiers can be used to detect malware samples by comparin
48#
發(fā)表于 2025-3-29 23:41:36 | 只看該作者
49#
發(fā)表于 2025-3-30 00:53:30 | 只看該作者
Heinrich Schmidt Prof. Dr. med.ch more difficult respectively. The traditional approaches for dealing with malware detection are not efficient anymore which paves the way for the adoption of machine learning algorithms as a solution for this issue. Also, there is a significant rise in malware concerned with Mac OS X devices due t
50#
發(fā)表于 2025-3-30 05:12:24 | 只看該作者
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