Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Abbasi, Mahmoud; Shahraki, Amin; Taherkordi, Amir

doi:10.1016/j.comcom.2021.01.021

Cited by 237 publications

(115 citation statements)

References 97 publications

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“…The data-driven methods are based on the statistical characteristics of network traffic history data, by using its self-similarity, long-term relevance, and periodicity to forecast the trend in time domain [1,28]. This kind of methods does not specifically analyze the specific dynamics and behaviors of network elements and has high flexibility.…”

Section: Relatedmentioning

confidence: 99%

“…Recently, the deep learning model has been effectively devoted to promote complex pattern recognition and analysis in big data systems [28,[39][40][41]. Meanwhile, with the improvement of network big data collection ability, network traffic prediction methods based on the deep learning have attracted much attention because of its ability on capturing the nonlinear features of network traffic [1,41].…”

Section: Relatedmentioning

confidence: 99%

“…For network traffic prediction, it may lose the ability of weighted long-term dependence, which makes it having limitations in long-term prediction. Two famous architectures proposed to solve this problem are LSTM and GRU,and they are improved variants of RNN, which have been proved to solve the above problems [18,28,54]. Here, we take the GRU model to find the temporal correlation of network traffic data which is relatively simple, because of less parameters and faster training speed.…”

Section: Spatial Relation Feature Extraction Modelmentioning

confidence: 99%

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[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

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Section: Relatedmentioning

confidence: 99%

Section: Relatedmentioning

confidence: 99%

Section: Spatial Relation Feature Extraction Modelmentioning

confidence: 99%

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[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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“…Many of the recently proposed mechanisms show that Machine Learning and Deep Learning methods achieve good results. The work presented in [23] surveyed Deep Learning-based classification models for network traffic classification. [24] uses machine learning techniques to identify the services within HTTPS connections.…”

Section: Motivationmentioning

confidence: 99%

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

et al. 2021

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As the 5G systems have introduced network slicing on the virtualized network environment, application-specific QoS (Quality of Service) management has come to the fore to support a range of services, such as eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communications), and mIoT (massive Internet of Things). It is expected for the next-generation systems to support more diverse applications demanding newer and granular quality characteristics for network services. Most of the current transport layer protocols, built on layering design and best-effort paradigm, do not provide the necessary precision and adaptability required for various QoS support. Therefore, we need new protocols that comprehend application behaviours and adapt to dynamic network conditions for more fine-grained QoS enhancement. This paper presents a Context-oriented Transport (CoT) layer for the next-generation network that understands the application context and adapts to varying network conditions with flowbased QoS control. CoT is an end-to-end software solution that improves the underlying network capacity utilization and prioritizes the traffic flows according to quality needs. We prototyped CoT in Linux/Android devices and evaluated the performance with emulated traffic environment. The experiments show that CoT reduces latency by up to 16.5% and improves average throughput by up to 30%, compared with Android 10 in LTE network, resulting from enhanced traffic classification and network utilization.

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“…It is worth mentioning that Abbasi et al in [24] present a survey of Deep Learning methods for network traffic monitoring and analysis, which addresses, among many things, fault management. However, most of the presented works target Radio Access Networks and Cyber Physical Networks, and are therefore not applicable to our IP core network scenario.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Network Status Detection and Fault Localization

Mohammed

Côté

et al. 2021

IEEE Trans. Instrum. Meas.

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Although the autonomous detection of network status and localization of network faults can be a valuable tool for network and service operators, very few works have investigated this subject. As a result in today's networks, fault detection and localization remains a mostly-manual process. In this paper, we propose a Machine Learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses Decision Tree, Gradient Boosting (GB), and XGBoost (XGB) ML algorithms to detect the network status as Normal, Congestion, and Network Fault. In comparison, existing related work can at best classify the network status as faulty or non-faulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.

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Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Cited by 237 publications

References 97 publications

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

A Method for Enabling Context-Awareness at Transport Layer for Improved Quality-of-Service Control

Machine Learning-Based Network Status Detection and Fault Localization

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