Biblio

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

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.

Traffic identification becomes more important yet more challenging as related encryption techniques are rapidly developing nowadays. In difference to recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. Based on this, we further provide a traffic classification framework in which unlabeled traffic is utilized to pre-train an encoding network that learns the contextual distribution of traffic payload bytes. Then, the downward classification reuses the pre-trained network to obtain an enhanced classification result. By implementing experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding alone with its pre-training strategy has been addressed.

Nowadays, encrypted traffic classification has become a challenge for network monitoring and cyberspace security. However, the existing methods cannot meet the requirements of encrypted traffic classification because of the encryption protocol in communication. Therefore, we design a novel neural network named Stereo Transform Neural Network (STNN) to classify encrypted network traffic. In STNN, we combine Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) based on statistical features. STNN gains average precision about 95%, average recall about 95%, average F1-measure about 95% and average accuracy about 99.5% in multi-classification. Besides, the experiment shows that STNN obviously accelerates the convergence rate and improves the classification accuracy.

With the unprecedented prevalence of mobile network applications, cryptographic protocols, such as the Secure Socket Layer/Transport Layer Security (SSL/TLS), are widely used in mobile network applications for communication security. The proven methods for encrypted video stream classification or encrypted protocol detection are unsuitable for the SSL/TLS traffic. Consequently, application-level traffic classification based networking and security services are facing severe challenges in effectiveness. Existing encrypted traffic classification methods exhibit unsatisfying accuracy for applications with similar state characteristics. In this paper, we propose a multiple-attribute-based encrypted traffic classification system named Multi-Attribute Associated Fingerprints (MAAF). We develop MAAF based on the two key insights that the DNS traces generated during the application runtime contain classification guidance information and that the handshake certificates in the encrypted flows can provide classification clues. Apart from the exploitation of key insights, MAAF employs the context of the encrypted traffic to overcome the attribute-lacking problem during the classification. Our experimental results demonstrate that MAAF achieves 98.69% accuracy on the real-world traceset that consists of 16 applications, supports the early prediction, and is robust to the scale of the training traceset. Besides, MAAF is superior to the state-of-the-art methods in terms of both accuracy and robustness.