MATEC: A lightweight neural network for online encrypted traffic classification

2021 ◽  
pp. 108472
Author(s):  
Jin Cheng ◽  
Yulei Wu ◽  
Yuepeng E ◽  
Junling You ◽  
Tong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Classification ◽  
Encrypted Traffic

scholarly journals Robot Communication: Network Traffic Classification Based on Deep Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengmeng Ge ◽  
Xiangzhan Yu ◽  
Likun Liu
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Classification ◽  
Classification Framework ◽  
Network Traffic Classification ◽  
Scalar Input ◽  
Memory Network ◽  
Encrypted Traffic ◽  
Robot Communication

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%

scholarly journals ICLSTM: Encrypted Traffic Service Identification Based on Inception-LSTM Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1080
Author(s):  
Bei Lu ◽  
Nurbol Luktarhan ◽  
Chao Ding ◽  
Wenhui Zhang
Keyword(s):  
Neural Network ◽  
Classification Method ◽  
Feature Engineering ◽  
Traffic Classification ◽  
Training Phase ◽  
Traffic Data ◽  
The Public ◽  
Service Identification ◽  
Key Features ◽  
Encrypted Traffic

The wide application of encryption technology has made traffic classification gradually become a major challenge in the field of network security. Traditional methods such as machine learning, which rely heavily on feature engineering and others, can no longer fully meet the needs of encrypted traffic classification. Therefore, we propose an Inception-LSTM(ICLSTM) traffic classification method in this paper to achieve encrypted traffic service identification. This method converts traffic data into common gray images, and then uses the constructed ICLSTM neural network to extract key features and perform effective traffic classification. To alleviate the problem of category imbalance, different weight parameters are set for each category separately in the training phase to make it more symmetrical for different categories of encrypted traffic, and the identification effect is more balanced and reasonable. The method is validated on the public ISCX 2016 dataset, and the results of five classification experiments show that the accuracy of the method exceeds 98% for both regular encrypted traffic service identification and VPN encrypted traffic service identification. At the same time, this deep learning-based classification method also greatly simplifies the difficulty of traffic feature extraction work.

scholarly journals CLD-Net: A Network Combining CNN and LSTM for Internet Encrypted Traffic Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xinyi Hu ◽  
Chunxiang Gu ◽  
Fushan Wei
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Virtual Private Network ◽  
Feature Reduction ◽  
Series Data ◽  
Traffic Classification ◽  
Lstm Network ◽  
Private Network ◽  
Encrypted Traffic

The development of the Internet has led to the complexity of network encrypted traffic. Identifying the specific classes of network encryption traffic is an important part of maintaining information security. The traditional traffic classification based on machine learning largely requires expert experience. As an end-to-end model, deep neural networks can minimize human intervention. This paper proposes the CLD-Net model, which can effectively distinguish network encrypted traffic. By segmenting and recombining the packet payload of the raw flow, it can automatically extract the features related to the packet payload, and by changing the expression of the packet interval, it integrates the packet interval information into the model. We use the ability of Convolutional Neural Network (CNN) to distinguish image classes, learn and classify the grayscale images that the raw flow has been preprocessed into, and then use the effectiveness of Long Short-Term Memory (LSTM) network on time series data to further enhance the model’s ability to classify. Finally, through feature reduction, the high-dimensional features learned by the neural network are converted into 8 dimensions to distinguish 8 different classes of network encrypted traffic. In order to verify the effectiveness of the CLD-Net model, we use the ISCX public dataset to conduct experiments. The results show that our proposed model can distinguish whether the unknown network traffic uses Virtual Private Network (VPN) with an accuracy of 98% and can accurately identify the specific traffic (chats, audio, or file) of Facebook and Skype applications with an accuracy of 92.89%.

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