Biblio

The fast growth of encrypted traffic puts forward burning requirements on the efficiency of traffic classification. Although deep learning models perform well in the classification, they sacrifice the efficiency to obtain high-precision results. To reduce the resource and time consumption, a novel and lightweight model is proposed in this paper. Our design principle is to “maximize the reuse of thin modules”. A thin module adopts the multi-head attention and the 1D convolutional network. Attributed to the one-step interaction of all packets and the parallelized computation of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of 1D convolutional networks are proved in traffic classification. Besides, the proposed model can work well in a real time manner, since only three consecutive packets of a flow are needed. To improve the stability of the model, the designed network is trained with the aid of ResNet, layer normalization and learning rate warmup. The proposed model outperforms the state-of-the-art works based on deep learning on two public datasets. The results show that our model has higher accuracy and running efficiency, while the number of parameters used is 1.8% of the 1D convolutional network and the training time halves.

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for website to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA, improve CAPTCHA technology, it can also promote the technologies of license plate recognition and handwriting recognition. This paper proposed a method based on Convolutional Neural Network (CNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over-fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion.