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Titlebook: Anomaly Detection as a Service; Challenges, Advances Danfeng Daphne Yao,Xiaokui Shu,Salvatore J. Stolfo Book 2018 Springer Nature Switzerla

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樓主: Flippant
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發(fā)表于 2025-3-23 12:02:11 | 只看該作者
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發(fā)表于 2025-3-23 15:59:53 | 只看該作者
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發(fā)表于 2025-3-23 20:26:29 | 只看該作者
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發(fā)表于 2025-3-23 23:01:18 | 只看該作者
Anomaly Detection as a Service978-3-031-02354-5Series ISSN 1945-9742 Series E-ISSN 1945-9750
15#
發(fā)表于 2025-3-24 02:48:52 | 只看該作者
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發(fā)表于 2025-3-24 09:21:13 | 只看該作者
Oblivious Transfer and Applicationsly detection [53]. We take the finitestate automaton (FSA) model as an instance, and present the event-aware FSA model, named eFSA, to detect stealthy anomalous CPS program behaviors particularly caused by data-oriented attacks.
17#
發(fā)表于 2025-3-24 12:06:32 | 只看該作者
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發(fā)表于 2025-3-24 14:54:53 | 只看該作者
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發(fā)表于 2025-3-24 22:46:11 | 只看該作者
Local vs. Global Program Anomaly Detection,aining data is assumed to be free of adversarial contaminations (e.g., [249]); or (ii) unsupervised learning, where the training data may contain detectable noise, e.g., training samples may include old exploits still propagating on the host or the Internet [64]. The latter case usually assumes that
20#
發(fā)表于 2025-3-25 00:08:43 | 只看該作者
Program Analysis in Program Analysis in Detection,ibing a couple of examples that use static program analysis in machine learning algorithms. The related description on using static dependency analysis for securing control programs in cyber-physical systems (CPS) is given in Chapter 5.
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