Low-rate Denial of Service (LDoS) attacks use the low-rate requests to achieve the occupation of the network resources and have strong concealment. The traditional signal analysis based detection methods are challenging to detect LDoS attacks in the fluctuating legitimate traffic. In this paper, an LDoS attack detection method based on hybrid deep neural networks is proposed using one-dimensional convolutional neural network and gated recurrent unit. In order to evaluate the proposed detection method in the real scenarios, we captured real legitimate traffic from a website in the datacenter, and carried out a variety of real LDoS attacks on the mirror of the website in the laboratory environment to obtain real attack traffic. The detection results on the real traffic show that the proposed detection method does not need to extract features manually and can effectively detect LDoS attacks in fluctuating HTTP traffic with an average detection rate of 98.68%, which is more advantageous than MF-DFA or power spectral density based detection methods.

低速率拒绝服务（LDoS）攻击利用低速率请求来实现对网络资源的占用，具有很强的隐蔽性。传统的基于信号分析的检测方法难以检测波动的合法流量中的 LDoS 攻击。本文利用一维卷积神经网络和门控循环单元，提出了一种基于混合深度神经网络的LDoS攻击检测方法。为了在真实场景中评估所提出的检测方法，我们从数据中心的某个网站捕获了真实的合法流量，并在实验室环境中对该网站的镜像进行了多种真实的 LDoS 攻击，以获得真实的攻击流量。