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A representative example of how rules for anomaly detection are defined and applied is given by the LEarning Rules for Anomaly Detection (LERAD) model [25, 26].
The Local Outlier Factor (LOF) [27] is an unsupervised approach, which is used in the LEarning Rules for Anomaly Detection (LERAD) model [25].
Efforts with the EINSTEIN program for anomaly detection (information sharing to detect odd behavior and protect against it) hasn't netted much; indeed there are reasons to expect that type of response can't be effective at large-scale.
In [7, 8], random projection in conjunction with principal component analysis (PCA) was implemented for anomaly detection in compressed domain, and an application of this proposed methodology to detect IP-level volume anomalies in computer network traffic suggested a high relevance to practical problems.
It uses the data for anomaly detection and control but "not [for] optimisation and prediction, which provide the greatest value", McKinsey points out.
High-dimensional problem domains pose significant challenges for anomaly detection.
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This problem, known as the 'curse of dimensionality', is an obstacle for many anomaly detection techniques.
This is in fact a technical enabler for future anomaly detection algorithms.
For HSI anomaly detection, Banerjee et al. propose a fast global SVDD detector [11].
This feature is an enabler for our anomaly detection and location-attribution algorithm.
The rules are used as input for further anomaly detection analysis to recognize more true positive alarm sequences.
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