Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

López-Martín, Manuel; Carro, Belén; Sánchez-Esguevillas, Antonio; Lloret, Jaime

doi:10.1109/access.2017.2747560

Cited by 587 publications

(302 citation statements)

References 28 publications

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“…For network classification tasks, mixed models are reported to outperform pure LSTM or CNN models [12]. To capture both spatial and temporal features of a flow, both CNN and RNN are used in [9], [12] for different applications. Aside from minor differences, both studies take the content of the first 6 to 30 packets to the CNN model followed by a RNN or LSTM model.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Time Series+Header: Since time series features are barely affected by encryption, it has been widely applied to various applications and datasets. The first few packets, from 10 to 30 packets, are reported to be enough for classification in many datasets [8], [12]. Sampled packets from the entire flow are also shown to achieve promising accuracy [3].…”

Section: F Model Selectionmentioning

confidence: 99%

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Deep Learning for Encrypted Traffic Classification: An Overview

2019

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Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy have been declined due to the dramatic changes in the Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Section: F Model Selectionmentioning

confidence: 99%

Deep Learning for Encrypted Traffic Classification: An Overview

2019

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“…While different statistical and machine learning tools have been used till now for traffic classification, e.g. refer to [16] and references herein, most of these works are dependent upon features which are either not available in encrypted traffic, or cannot be extracted in real time, e.g. port number and payload data [16,20].…”

Section: Related Work and Research Gapmentioning

confidence: 99%

Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Azari

Papapetrou

Denic

et al. 2019

Lecture Notes in Computer Science

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Prediction of user traffic in cellular networks has attracted profound attention for improving resource utilization. In this paper, we study the problem of network traffic traffic prediction and classification by employing standard machine learning and statistical learning time series prediction methods, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), respectively. We present an extensive experimental evaluation of the designed tools over a real network traffic dataset. Within this analysis, we explore the impact of different parameters to the effectiveness of the predictions. We further extend our analysis to the problem of network traffic classification and prediction of traffic bursts. The results, on the one hand, demonstrate superior performance of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. On the other hand, the results shed light on the circumstances in which, ARIMA performs close to the optimal with lower complexity.

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“…The experiment uses a dump of network traffic [21][22][23][24][25][26][27][28][29] with 14 different traffic type tags generated by different applications (7 for conventional encrypted traffic and 7 for VPN traffic). The quality criterion for traffic classification is the accuracy of classifying samples.…”

Section: Realization Of Network Traffic Analysis Algorithms and Expermentioning

confidence: 99%

Network traffic analyzing algorithms on the basis of machine learning methods

Battalov¹,

Nikonov

Gayanova³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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Traffic analysis systems are widely used in monitoring the network activity of users or a specific user and restricting client access to certain types of services (VPN, HTTPS) which makes content analysis impossible. Algorithms for classifying encrypted traffic and detecting VPN traffic are proposed. Three algorithms for constructing classifiers are considered -MLP, RFT and KNN. The proposed classifier demonstrates recognition accuracy on a test sample up to 80%. The MLP, RFT and KNN algorithms had almost identical performance in all experiments. It was also found that the proposed classifiers work better when the network traffic flows are generated using short values of the time parameter (timeout). The novelty lies in the development of network traffic analysis algorithms based on a neural network, differing in the method of selection, generation and selection of features, which allows to classify the existing traffic of protected connections of selected users according to a predetermined set of categories.

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Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

Cited by 587 publications

References 28 publications

Deep Learning for Encrypted Traffic Classification: An Overview

Deep Learning for Encrypted Traffic Classification: An Overview

Cellular Traffic Prediction and Classification: A Comparative Evaluation of LSTM and ARIMA

Network traffic analyzing algorithms on the basis of machine learning methods

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