  Detecting and intercepting malicious requests are one of the most widely used
ways against attacks in the network security. Most existing detecting
approaches, including matching blacklist characters and machine learning
algorithms have all shown to be vulnerable to sophisticated attacks. To address
the above issues, a more general and rigorous detection method is required. In
this paper, we formulate the problem of detecting malicious requests as a
temporal sequence classification problem, and propose a novel deep learning
model namely Convolutional Neural Network-Bidirectional Long Short-term
Memory-Convolutional Neural Network (CNN-BiLSTM-CNN). By connecting the shadow
and deep feature maps of the convolutional layers, the malicious feature
extracting ability is improved on more detailed functionality. Experimental
results on HTTP dataset CSIC 2010 have demonstrated the effectiveness of the
proposed method when compared with the state-of-the-arts.
