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On the Use of VGs for Feature Selection in Supervised Machine Learning - A Use Case to Detect Distributed DoS Attacks

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Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1981))

Abstract

Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised Machine Learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.

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Acknowledgements

This work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project “Cybers SeC IP” (NORTE-01-0145-FEDER-000044).

Author information

Authors and Affiliations

  1. ADiT-Lab, Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal

    João Lopes & Pedro Pinto

  2. Data, Complex Networks and Cybersecurity Sciences Technological Institute, Rey Juan Carlos University, Madrid, Spain

    Alberto Partida

  3. Universidade da Maia, Maia, Portugal

    Pedro Pinto

  4. CIICESI, ESTG, Instituto Politécnico do Porto, Felgueiras, Portugal

    António Pinto

  5. INESC TEC, Porto, Portugal

    Pedro Pinto & António Pinto

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  1. João Lopes
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  2. Alberto Partida
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  4. António Pinto
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Correspondence to João Lopes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

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Lopes, J., Partida, A., Pinto, P., Pinto, A. (2024). On the Use of VGs for Feature Selection in Supervised Machine Learning - A Use Case to Detect Distributed DoS Attacks. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-53025-8_19

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