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Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Author

Listed:
  • Konstantinos Psychogyios

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece)

  • Andreas Papadakis

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece)

  • Stavroula Bourou

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece)

  • Nikolaos Nikolaou

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece)

  • Apostolos Maniatis

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece)

  • Theodore Zahariadis

    (Synelixis Solutions S.A., 10 Farmakidou Av., 34100 Chalkida, Greece
    General Department, National and Kapodistrian University of Athens, 15772 Athens, Greece)

Abstract

The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

Suggested Citation

  • Konstantinos Psychogyios & Andreas Papadakis & Stavroula Bourou & Nikolaos Nikolaou & Apostolos Maniatis & Theodore Zahariadis, 2024. "Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data," Future Internet, MDPI, vol. 16(3), pages 1-16, February.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:3:p:73-:d:1344394
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    References listed on IDEAS

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    1. Thi-Thu-Huong Le & Yustus Eko Oktian & Howon Kim, 2022. "XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
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