High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

• Published in: Pattern recognition Vol. 58; pp. 121 - 134
• Main Authors: Erfani, Sarah M., Rajasegarar, Sutharshan, Karunasekera, Shanika, Leckie, Christopher
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2016
• Subjects: Anomalies; Anomaly detection; Architecture; Belief networks; Deep belief net; Deep learning; Detectors; Feature extraction; High-dimensional data; Learning; Obstacles; One-class SVM; Outlier detection; Support vector machines; High-dimensional data; Deep learning; One-class SVM; Deep belief net; Outlier detection; Feature extraction; Anomaly detection

High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the ‘curse of dimensionality’, is an obstacle for many anomaly detection techniques. Building a robust anomaly detecti...