Machine Learning Based Approach to Anomaly and Cyberattack Detection in Streamed Network Traffic Data

Details

Abstract

In this paper, the performance of a solution providing stream processing is evaluated, and its accuracy in the classification of suspicious flows in simulated network traffic is investigated. The concept of the solution is fully disclosed along with its initial evaluation in a real-world environment. The proposition features Apache Kafka for efficient communication among different applications, along with Elasticsearch and Kibana as storage and visualisation solutions. At the heart of the engine are machine learning algorithms implemented using the TensorFlow library, providing the cutting edge in network intrusion detection. The tool allows easy definition of streams and implementation of any machine learning algorithm.

Authors

• Mikołaj Komisarek
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  ITTI Sp. z o.o. | UTP University of Science and Technology
  Poznań, Poland | Bydgoszcz, Poland
• Marek Pawlicki
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  ITTI Sp. z o.o. | UTP University of Science and Technology
  Poznań, Poland | Bydgoszcz, Poland
• Rafał Kozik
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  ITTI Sp. z o.o. | UTP University of Science and Technology
  Poznań, Poland | Bydgoszcz, Poland
• Michał Choraś
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  FernUniversität in Hagen | UTP University of Science and Technology
  Hagen, Germany | Bydgoszcz, Poland