Real-time stream processing tool for detecting suspicious network patterns using machine learning
| Autor: | Rafał Kozik, Michał Choraś, Marek Pawlicki, Mikołaj Komisarek |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Computer science business.industry Big data 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre Random forest Stream processing 020204 information systems 0202 electrical engineering electronic engineering information engineering Anomaly detection Artificial intelligence business Stream data computer |
| Zdroj: | ARES ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security |
| DOI: | 10.1145/3407023.3409189 |
| Popis: | In this paper, the performance of stream processing and accuracy in the prediction of suspicious flows in simulated network traffic is investigated. In addition, concepts of an engine that integrates with novel solutions like the Elastic-search database and Apache Kafka that allows easy definition of streams and implementation of any machine learning algorithm are presented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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