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BigFlow: Real-time and reliable anomaly-based intrusion detection for high-speed networks

机译：BigFlow：用于高速网络的实时，可靠的基于异常的入侵检测

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摘要

Existing machine learning solutions for network-based intrusion detection cannot maintain their reliability over time when facing high-speed networks and evolving attacks. In this paper, we propose BigFlow, an approach capable of processing evolving network traffic while being scalable to large packet rates. BigFlow employs a verification method that checks if the classifier outcome is valid in order to provide reliability. If a suspicious packet is found, an expert may help BigFlow to incrementally change the classification model. Experiments with BigFlow, over a network traffic dataset spanning a full year, demonstrate that it can maintain high accuracy over time. It requires as little as 4% of storage and between 0.05% and 4% of training time, compared with other approaches. BigFlow is scalable, coping with a 10-Gbps network bandwidth in a 40-core cluster commodity hardware. (C) 2018 Elsevier B.V. All rights reserved.
机译：当面对高速网络和不断发展的攻击时，用于基于网络的入侵检测的现有机器学习解决方案无法长期保持其可靠性。在本文中，我们提出了BigFlow，这是一种能够处理不断发展的网络流量同时又可扩展至大数据包速率的方法。 BigFlow采用一种验证方法，该方法检查分类器结果是否有效以提供可靠性。如果找到可疑数据包，专家可以帮助BigFlow逐步更改分类模型。在长达一整年的网络流量数据集上对BigFlow进行的实验表明，它可以长期保持较高的准确性。与其他方法相比，它仅需要4％的存储空间和0.05％到4％的训练时间。 BigFlow具有可扩展性，可以在40核群集商品硬件中处理10 Gbps的网络带宽。 （C）2018 Elsevier B.V.保留所有权利。

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