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The article considers one of the key problems of microservice systems associated with the rapid growth of threats to container applications, containerization and orchestration tools. The emergence of new attack vectors, as well as continuous improvement of the existing ones, creates serious difficulties in the implementation of such systems and technologies. This article presents an approach to the implementation of a software component for detecting anomalous packets in the network traffic of container systems. The approach is based on the frequency analysis of the payload of network traffic packets, constructing histograms of fixed-size network packets and using them both for training the Autoencoder (AE) – Long short-term memory (LSTM) hybrid neural network model and for subsequent detection. The experimental results of the proposed approach show a fast training process and high accuracy in detecting anomalous packets in network traffic. Additionally, the low rate of false positives makes the proposed solution suitable as an extra layer for intrusion detection in container systems.

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Detecting Anomalies in Containerized Systems: Applying Frequency Analysis to Network Packet Payloads Using AE-LSTM Hybrid Neural Network

  • 会议论文

  • 发表日期：

    2026年

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  • Igor Kotenko,
  • Maxim Melnik,
  • Georgii Abramenko

摘要

The article considers one of the key problems of microservice systems associated with the rapid growth of threats to container applications, containerization and orchestration tools. The emergence of new attack vectors, as well as continuous improvement of the existing ones, creates serious difficulties in the implementation of such systems and technologies. This article presents an approach to the implementation of a software component for detecting anomalous packets in the network traffic of container systems. The approach is based on the frequency analysis of the payload of network traffic packets, constructing histograms of fixed-size network packets and using them both for training the Autoencoder (AE) – Long short-term memory (LSTM) hybrid neural network model and for subsequent detection. The experimental results of the proposed approach show a fast training process and high accuracy in detecting anomalous packets in network traffic. Additionally, the low rate of false positives makes the proposed solution suitable as an extra layer for intrusion detection in container systems.