Encrypted Traffic Classification Based ML for Identifying Different Social Media Applications

Al-Obaidy, Furat; Momtahen, Shadi; Hossain, Md.Foysal; Mohammadi, Fazel

doi:10.1109/ccece.2019.8861934

Cited by 22 publications

(6 citation statements)

References 8 publications

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“…Al-Obaidy [20] et al presented a machine learning-based approach to classify and detect Skype, Whatsapp, Facebook, Netflix and Youtube applications communicating over the encrypted channel. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naive Bayes and C4.5 machine learning algorithms were preferred within the scope of the study.…”

Section: Related Workmentioning

confidence: 99%

A new classification method for encrypted internet traﬀic using machine learning

Uğurlu¹,

Doğru²,

Arslan³

2021

Turk J Elec Eng & Comp Sci

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The rate of internet usage in the world is over 62% and this rate is increasing day by day. With this increase, it becomes important to ensure the confidentiality of the information in the traffic flowing over the internet. Encryption algorithms and protocols are used for this purpose. This situation, which is beneficial for normal users, is also used by attackers to hide. Cyber attackers or hackers gain the ability to bypass security precautions such as IDS/IPS and antivirus systems with using encrypted traffic. Since payload analysis cannot be performed without deciphering the encrypted traffic, existing commercial security solutions fall short in this situation. In this study, it is aimed to classify the network traffic by analysing the outgoing and incoming data over the encrypted traffic using Extreme Gradient Boosting (XGBoost), Decision Tree and Random Forest classification methods. Thus, without deciphering, it is possible to classify packets passing through encrypted traffic using some metadata like size and duration and to take precautions against attacks. ISCX VPN-NonVPN dataset was used to test the proposed model in this study. With the created framework, encrypted traffic was classified with a high success rate and 94.53% success was achieved by using the XGBoost classification method.

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Section: Related Workmentioning

confidence: 99%

A new classification method for encrypted internet traﬀic using machine learning

Uğurlu¹,

Doğru²,

Arslan³

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Other works are known in the state-of-the-art, proposing a network traffic classification targeting security, quality of service enhancement, management, and others [14], [15], [16]. They vary in terms of the learning and validation method, also differs between strategies based on port, payload, statistics, CNNs, flows, and others [17].…”

Section: Related Workmentioning

confidence: 99%

Improving the network traffic classification using the Packet Vision approach

Moreira¹,

Rodrigues²,

Rosa³

et al. 2020

Anais Do XVI Workshop De Visão Computacional (WVC 2020)

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The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.

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“…Deep learning has emerged as a highly desirable approach for traffic classification as an end-to-end method. It is able to learn the nonlinear relationship between the original input and the corresponding output without decomposing the problem into subproblems of feature selection and classification [6,7]. One of the advantages of deep learning is higher learning capability than the traditional ML methods [8].…”

Section: Introductionmentioning

confidence: 99%

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

Luktarhan

Ding

et al. 2021

Symmetry

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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.

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Encrypted Traffic Classification Based ML for Identifying Different Social Media Applications

Cited by 22 publications

References 8 publications

A new classification method for encrypted internet traﬀic using machine learning

A new classification method for encrypted internet traﬀic using machine learning

Improving the network traffic classification using the Packet Vision approach

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

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