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.

中文翻译：

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MineCap：超级增量学习，用于在软件定义的网络上检测和阻止加密货币挖掘

秘密加密货币的挖掘意味着要使用宝贵的计算资源和高能耗。在本文中，我们提出了MineCap，这是一种动态的在线机制，可通过在软件定义的网络上使用机器学习来检测和阻止秘密加密货币的挖掘流程。所提出的机制依赖于Spark Streaming对网络流进行在线处理，并且在识别挖掘流时，它会向网络控制器请求流阻止。我们还提出了一种称为“超级增量学习”的学习技术，这是应用于在线学习的超级学习器的一种变体，该技术将一组分类器的分类概率作为增量学习分类器的特征。因此，我们设计了一种精确的机制来对挖掘流程进行分类，这些挖掘流程可以根据输入数据平均以98％的准确度进行学习，