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Annals of Telecommunications
Erschienen in: Annals of Telecommunications 3-4/2020

08.01.2020

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

verfasst von: Helio N. Cunha Neto, Martin Andreoni Lopez, Natalia C. Fernandes, Diogo M. F. Mattos

Erschienen in: Annals of Telecommunications | Ausgabe 3-4/2020

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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.
Vorheriger Artikel A break-glass protocol based on ciphertext-policy attribute-based encryption to access medical records in the cloud
Nächster Artikel Improving threat detection in networks using deep learning

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Fußnoten
4
The mining traffic used to train the machine learning algorithms originates from the execution of the cpuminer and the xmrig mining applications.
 
6
The datasets are available upon email requests to the authors.
 
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Metadaten
Titel
MineCap: super incremental learning for detecting and blocking cryptocurrency mining on software-defined networking
verfasst von
Helio N. Cunha Neto
Martin Andreoni Lopez
Natalia C. Fernandes
Diogo M. F. Mattos
Publikationsdatum
08.01.2020
Verlag
Springer International Publishing
Erschienen in
Annals of Telecommunications / Ausgabe 3-4/2020
Print ISSN: 0003-4347
Elektronische ISSN: 1958-9395
DOI
https://doi.org/10.1007/s12243-019-00744-4

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