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Real-time GPU-based timing channel detection using entropy

机译：基于熵的基于GPU的实时定时通道检测

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As line rates continue to grow, network security applications such as covert timing channel (CTC) detection must utilize new techniques for processing network flows in order to protect critical enterprise networks. GPU-based packet processing provides one means of scaling the detection of CTCs and other anomalies in network flows. In this paper, we implement a GPU-based detection tool, capable of detecting model-based covert timing channels (MBCTCs). The GPU's ability to process a large number of packets in parallel enables more complex detection tests, such as the corrected conditional entropy (CCE) test-a modified version of the conditional entropy measurement, which has a variety of applications outside of covert channel detection. In our experiments, we evaluate the CCE test's true and false positive detection rates, as well as the time required to perform the test on the GPU. Our results demonstrate that GPU packet processing can be applied successfully to perform real-time CTC detection at near 10 Gbps with high accuracy.
机译：随着线速的不断增长，网络安全应用（例如隐蔽定时信道（CTC）检测）必须利用新技术来处理网络流，以保护关键的企业网络。基于GPU的数据包处理提供了一种扩展对CTC和网络流中其他异常的检测的方法。在本文中，我们实现了一个基于GPU的检测工具，该工具能够检测基于模型的隐式定时通道（MBCTC）。 GPU能够并行处理大量数据包，从而能够进行更复杂的检测测试，例如校正后的条件熵（CCE）测试-条件熵测量的改进版本，它在隐蔽通道检测之外具有多种应用。在我们的实验中，我们评估了CCE测试的正确和错误阳性检测率，以及在GPU上执行测试所需的时间。我们的结果表明，GPU数据包处理可以成功地应用于以接近10 Gbps的精度执行实时CTC检测。

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