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MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking

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A Correction to this article was published on 29 January 2020

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Abstract

Covert mining of cryptocurrency implies the use of valuable computing resources and high energy consumption. In this paper, we propose MineCap, a dynamic online mechanism for detecting and blocking covert cryptocurrency mining flows, using machine learning on software-defined networking. The proposed mechanism relies on Spark Streaming for online processing of network flows, and, when identifying a mining flow, it requests the flow blocking to the network controller. We also propose a learning technique called super incremental learning, a variant of the super learner applied to online learning, which takes the classification probabilities of an ensemble of classifiers as features for an incremental learning classifier. Hence, we design an accurate mechanism to classify mining flows that learn with incoming data with an average of 98% accuracy, 99% precision, 97% sensitivity, and 99.9% specificity and avoid concept drift–related issues.

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  • 29 January 2020

    The funding information in the original manuscript is incorrect, the correct information should be the below:

Notes

  1. Available on https://github.com/mininet/mininet.

  2. Available on https://osrg.github.io/ryu/.

  3. Available at https://github.com/DanielArndt/flowtbag.

  4. The mining traffic used to train the machine learning algorithms originates from the execution of the cpuminer and the xmrig mining applications.

  5. Available at https://github.com/appneta/tcpreplay.

  6. The datasets are available upon email requests to the authors.

  7. Available at https://minergate.com.

  8. Available at https://guiminer.org.

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Acknowledgements

We would like to acknowledge CNPq, CAPES, FAPERJ, RNP, and the ANEEL’s R&D program (PD-07130-0053/2018) for the partial funding of this research.

Funding

We would like to acknowledge CNPq, CAPES, FAPERJ, and RNP for the partial funding of this research.

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Authors and Affiliations

  1. MídiaCom/PPGEET/TET Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil

    Helio N. Cunha Neto, Natalia C. Fernandes & Diogo M. F. Mattos

  2. Samsung Research Institute, Campinas, SP, Brazil

    Martin Andreoni Lopez

Authors
  1. Helio N. Cunha Neto
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  2. Martin Andreoni Lopez
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  3. Natalia C. Fernandes
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  4. Diogo M. F. Mattos
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Corresponding author

Correspondence to Helio N. Cunha Neto.

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Neto, H.N.C., Lopez, M.A., Fernandes, N.C. et al. MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking. Ann. Telecommun. 75, 121–131 (2020). https://doi.org/10.1007/s12243-019-00744-4

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