Detecting contextual collective anomalies at a Glance
| Autor: | Andrés Gago-Alonso, Mario Alfonso Prado-Romero |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Context (language use) 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Task (project management) 020204 information systems Outlier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Algorithm design Data mining Artificial intelligence Cluster analysis business computer |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr.2016.7900017 |
| Popis: | Many phenomena in our world can be modeled as networks, from neurons in the human brain, computer networks and bank transactions to social interactions. Anomaly detection is an important data mining task consisting in detecting rare objects that deviate from the majority of the data. Contextual collective anomaly detection techniques can be applied to intrusion detection in computer networks, bank fraud detection, or finding people with strange behavior in social networks. In this work, a fast and intuitive algorithm to detect collective contextual anomalies is presented. Furthermore, the importance of selecting algorithms which find meaningful outliers for the application domain specialists is analyzed. |
| Databáze: | OpenAIRE |
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