Journal
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Volume 13, Issue 6, Pages 1446-1459Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2018.2790580
Keywords
Anomaly detection; semi-supervised classification; maximum entropy; maximum margin classifier; support vector machines
Funding
- Consortium for Verification Technology under Department of Energy National Nuclear Security Administration [DE-NA0002534]
- University of Michigan ECE Departmental Fellowship
- Xerox University Grant
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Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have high real-world utility, while ignoring the low-utility statistically anomalous instances. To this end, we propose a novel method called Latent Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution. This method uses the Entropy Minimization (EM) algorithm to simultaneously incorporate the Geometric EM principle for identifying statistical anomalies, and the MED principle to incorporate utility labels, in order to detect high-utility anomalies. We apply our method in both simulated and real datasets to demonstrate that it has superior performance over existing alternatives that independently pre-process with unsupervised anomaly detection algorithms before classifying.
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