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Adaptive Reservoir Neural Gas: An Effective Clustering Algorithm for Addressing Concept Drift in Real-Time Data Streams

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14259))

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Abstracts

The concept drift phenomenon describes how the statistical properties of a data distribution change over time. In cybersecurity domain, where data arrives continuously and rapidly in a sequential manner, concept drift can be a significant challenge. Identifying concept drift, it enables security analysts to detect emerging attacks, respond promptly, and make informed decisions based on the changing nature of the data being analyzed. The Adaptive Reservoir Neural Gas (AR-NG) clustering algorithm is proposed in this paper to handle concept drift in real-time data streams. It is a novel approach that combines reservoir computing power with the neural gas algorithm, allowing the algorithm to automatically update its clustering structure as new data arrives. Furthermore, in order to effectively handle evolving data streams that significantly change over time in unexpected ways, the proposed method incorporates a density-based clustering mechanism (DBCM) to concept drift detection. Experiments on real-time data streams show that the proposed algorithm is effective at mitigating the impact of concept drift, making it a useful tool for real-time data analysis and decision-making in dynamic environments.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Informatics Studies, Hellenic Open University, Patra, Greece

    Konstantinos Demertzis

  2. School of Engineering, Department of Civil Engineering, Faculty of Mathematics Programming and General Courses, Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis & Antonios Papaleonidas

Authors
  1. Konstantinos Demertzis
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  2. Lazaros Iliadis
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  3. Antonios Papaleonidas
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Corresponding author

Correspondence to Konstantinos Demertzis .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Demertzis, K., Iliadis, L., Papaleonidas, A. (2023). Adaptive Reservoir Neural Gas: An Effective Clustering Algorithm for Addressing Concept Drift in Real-Time Data Streams. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14259. Springer, Cham. https://doi.org/10.1007/978-3-031-44223-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-44223-0_13

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