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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opprobrious

Anomaly Detection as a Service978-3-031-02354-5Series ISSN 1945-9742 Series E-ISSN 1945-9750

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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.

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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

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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.