An increasing number of Internet application services are relying on encrypted traffic to offer adequate consumer privacy. Anomaly detection in encrypted traffic to circumvent and mitigate cyber se...

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Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning

In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistical features calculated by raw traffic directly. The statistical features can be used to supplement the information loss due to trimming. After that, the outputs from the CNN and the encoder in SAE are combined into new high-level features; these new features include the information from the trimmed raw traffic and statistical features. Finally, these new high-level features are used to classify encrypted traffic. “ISCX VPNnonVPN” traffic dataset is used to demonstrate the feasibility of this framework. The framework proposed in this paper can improve the performance of encrypted traffic classification; it achieves an f1-score of 0.98. Furthermore, new high-level features, which generated by combining the features extracted from a convolutional neural network and a stacked autoencoder, can represent different classes of traffic well. More importantly, this work is unique in the encrypted traffic classification field, for it is the first time to use both raw traffic and statistical features as the input of the model.

An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders

MTT: an efficient model for encrypted network traffic classification using multi-task transformer

The rapidly growing encrypted traffic hides a large number of malicious behaviours. The difficulty of collecting and labelling encrypted traffic makes the class distribution of dataset seriously imbalanced, which leads to the poor generalisation ability of the classification model. To solve this problem, a new representation learning method in encrypted traffic and its diversity enhancement model are proposed, which uses the diversity of images to represent the diversity of traffic samples. First, the encrypted traffic is transformed into Markov images. Then, a diversity maximisation Markov-GAN based on the Simpson index is designed to generate new Markov images. Finally, the balanced Markov image set is sent to the CNN for classification. Experimental results show that the proposed method can predict the whole dataset space with only a few original samples. And the classification accuracies under different imbalance degrees are significantly improved, all of which are over 90%. The enhanced Markov image set can effectively alleviate performance generalisation deviation caused by different network depths. Even an ordinary CNN has almost the same classification effect as VGG13 and VGG16. Compared with other data enhancement methods, the Markov-GAN only needs to balance the transform domain dataset, which is lightweight, easy to train and has stronger amplification ability. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ise2.12071

Markov-GAN: Markov image enhancement method for malicious encrypted traffic classification

Encrypted network traffic classification based on machine learning

With the widespread application of virtual private network (VPN) technology, real-time VPN traffic identification has become an increasingly important task in network management and security maintenance. Since traditional encrypted traffic identification technology is not effective in feature extraction and selection, this paper proposes two deep learning-based models to classify the traffic into VPN and non-VPN traffic, identify VPN traffic generated by six different applications much further. Our models utilize convolutional auto-encoding (CAE) and convolutional neural network (CNN), respectively, preprocessing the traffic samples into session pictures, to accomplish the experiment objectives. The CAE-based method, utilizing the unsupervised nature of CAE to extract the hidden layer features, can automatically learn the nonlinear relationship between original input and expected output. The CNN-based method performs well in extracting two-dimensional local features of images. Experimental results show that our models perform better than traditional identification methods. In the two-category identification, the best result comes from the CAE-based model; the overall identification accuracy rate is 98.77%. Among the six-category identification, the best result comes from CNN-based model; the overall identification accuracy rate is 92.92%.

Deep learning-based real-time VPN encrypted traffic identification methods

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Deep learning-based real-time VPN encrypted traffic identification methods