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A Study of Network Covert Channel Detection Based on Deep Learning

机译：基于深度学习的网络隐蔽通道检测研究

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摘要

Information security has become a growing concern. Computer covert channel which is regarded as an important area of information security research gets more attention. In order to detect these covert channels, a variety of detection algorithms are proposed in the course of the research. The algorithms of machine learning type show better results in these detection algorithms. However, the common machine learning algorithms have many problems in the testing process and have great limitations. Based on the deep learning algorithm, this paper proposes a new idea of network covert channel detection and forms a new detection model. On the one hand, this algorithmic model can detect more complex covert channels and, on the other hand, greatly improve the accuracy of detection due to the use of a new deep learning model. By optimizing this test model, we can get better results on the evaluation index.
机译：信息安全已成为人们日益关注的问题。作为信息安全研究的重要领域的计算机隐蔽渠道受到了越来越多的关注。为了检测这些隐蔽通道，在研究过程中提出了多种检测算法。机器学习类型的算法在这些检测算法中显示出更好的结果。但是，常见的机器学习算法在测试过程中存在很多问题，并且存在很大的局限性。基于深度学习算法，提出了网络隐蔽信道检测的新思路，并形成了新的检测模型。一方面，该算法模型可以检测到更复杂的隐蔽通道，另一方面，由于使用了新的深度学习模型，极大地提高了检测的准确性。通过优化该测试模型，我们可以在评估指标上获得更好的结果。

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