Enhancing one-class support vector machines for unsupervised anomaly detection

By Mennatallah Amer, Markus Goldstein, Slim Abdennadher. ODD '13, 2013

453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/eea4ca46542125e02cd7b6de60f28c3710b3f7a3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Manuel Günther
2. Andras Rozsa
3. Terrance E. Boult
4. Ethan M. Rudd
5. Gilberto Fernandes
6. Luiz Fernando Carvalho
7. Joel J. P. C. Rodrigues
8. Mario Lemes Proença Jr.

In Computer Networks and Communications

1. Andras Rozsa
2. Terrance E. Boult
3. Manuel Günther
4. Ethan M. Rudd

Citations in the corpus

• A comprehensive survey on network anomaly detection (2018, background): “Amer et al. [113] applied two modifications of the unsupervised one-class SVM: Robust one-class SVMs and eta one-class SVMs.”
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018, methodology): “, one-class SVM [1, 7], clustering based methods [9] like K-Means [26] and GMM [21], KDE [27], and VAE [2] and VRNN [33].”
• A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “One-class SVMs are popular nonlinear models, which have been successfully applied to detecting novel intrusions [118], [119], [120], [121], [122], [123].”