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NBcoded: Network Attack Classifiers Based on Encoder and Naive Bayes Model for Resource Limited Devices

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1525))

Abstract

In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, or network, can cause huge damage for the companies. However, when this attack detection encounters the Artificial Intelligence paradigm, it can be addressed using high-quality classifiers which often need high resource demands in terms of computation or memory usage. This situation has a high impact when the attack classifiers need to be used with limited resourced devices or without overloading the performance of the devices, as it happens for example in IoT devices, or in industrial systems. For overcoming this issue, NBcoded, a novel light attack classification tool is proposed in this work. NBcoded works in a pipeline combining the removal of noisy data properties of the encoders with the low resources and timing consuming obtained by the Naive Bayes classifier. This work compares three different NBcoded implementations based on three different Naive Bayes likelihood distribution assumptions (Gaussian, Complement and Bernoulli). Then, the best NBcoded is compared with state of the art classifiers like Multilayer Perceptron and Random Forest. Our implementation shows to be the best model reducing the impact of training time and disk usage, even if it is outperformed by the other two in terms of Accuracy and F1-score (\({\sim }2\)%).

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Notes

  1. 1.

    https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/.

  2. 2.

    https://qosient.com/argus/index.shtml.

  3. 3.

    https://zeek.org/.

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Acknowledgements

This work has been partially supported by the Spanish Centre for the Development of Industrial Technology (CDTI) under the project ÉGIDA (EXP 00122721/CER-20191012) - RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD and by the Basque Country Government under the ELKARTEK program, project TRUSTIND (KK-2020/00054).

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Authors and Affiliations

  1. Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia, Spain

    Lander Segurola-Gil, Francesco Zola, Xabier Echeberria-Barrio & Raul Orduna-Urrutia

  2. Institute of Smart Cities, Public University of Navarre, 31006, Pamplona, Spain

    Francesco Zola

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  1. Lander Segurola-Gil
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  4. Raul Orduna-Urrutia
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Correspondence to Lander Segurola-Gil .

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  1. IKIM, Ruhr-University Bochum, Bochum, Germany

    Michael Kamp

  2. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  3. University of Namur, Namur, Belgium

    Adrien Bibal

  4. University of Rennes 1, Rennes, France

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  5. University of Namur, Namur, Belgium

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  6. Inria, Rennes, France

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  7. University of Antwerp, Antwerp, Belgium

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  8. Ruhr University Bochum, Bochum, Germany

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  9. Royal Holloway University of London, Egham, UK

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  11. Université Jean Monnet, Saint-Etienne cedex 2, France

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  13. Telecom Paris, Paris, France

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  15. Norwegian Univesity of Science and Technology, Trondheim, Norway

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Segurola-Gil, L., Zola, F., Echeberria-Barrio, X., Orduna-Urrutia, R. (2021). NBcoded: Network Attack Classifiers Based on Encoder and Naive Bayes Model for Resource Limited Devices. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-93733-1_4

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