Real-time stream processing tool for detecting suspicious network patterns using machine learning

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Details

DOI: 10.1145/3407023.3409189
Publication type: Conference paper
Conference: ARES 2020: International Conference on Availability, Reliability and Security
Location: Virtual
Online publication date: 2020-08-25

Abstract

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.

Authors

  • Mikołaj Komisarek
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    UTP University of Science and Technology
    Bydgoszcz, Poland
  • Michał Choraś
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    UTP University of Science and Technology
    Bydgoszcz, Poland
  • Rafał Kozik
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    UTP University of Science and Technology
    Bydgoszcz, Poland
  • Marek Pawlicki
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    UTP University of Science and Technology
    Bydgoszcz, Poland