MSTNN: A Graph Learning Based Method for the Origin-Destination Traffic Prediction

Yang, Chenming; Zhang, Zhiheng; Wen, Hui; Zhou, Liang

doi:10.1109/icc40277.2020.9149277

Cited by 6 publications

(12 citation statements)

References 7 publications

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“…[35,36] discuss how to utilize GNN in network calculus analysis. GNN is also helpful in link delay prediction [37] and network traffic prediction [38][39][40]. GNN has been introduced into automatic detection for Botnets, which is important to prevent DDoS attacks [41].…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Ding¹,

Luo²,

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

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Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase generalization capabilities. However, MLP is not so suitable for graph-structured data like networks. MLP treats IP addresses as isolated instances and ignores the connection information, which limits geolocation accuracy. In this work, we research how to increase the generalization capability with an emerging graph deep learning method -Graph Neural Network (GNN). First, IP geolocation is re-formulated as an attributed graph node regression problem. Then, we propose a GNN-based IP geolocation framework named GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP) layers and a decoder. The preprocessor and encoder transform measurement data into the initial node embeddings. MP layers refine the initial node embeddings by modeling the connection information. The decoder maps the refined embeddings to nodes' locations and relieves the convergence problem by considering prior knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America, Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art rule-based and learning-based baselines. This work verifies the great potential of GNN for fine-grained IP geolocation.

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Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Ding¹,

Luo²,

Wang

et al. 2023

IEEE Trans. Netw. Sci. Eng.

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“…One of the advantages of the attention mechanism is that it focuses on the most relevant neighbors of the flow to make decisions, rather than taking the neighbors equally. Multi-scale Spatial-Temporal Graph Neural Network (MSTNN) [26] uses the attention mechanism on the spatial extractor for capturing the timevarying spatial correlations of nodes in origin-destination traffic prediction task. Wan et al proposed GLAD-PAW [24] for anomaly detection in log files.…”

Section: Related Workmentioning

confidence: 99%

Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

Chang¹,

Branco²

2021

Preprint

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The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection, new algorithms that are more robust, effective, and able to use more information are needed. Moreover, the intrusion detection task faces a serious challenge associated with the extreme class imbalance between normal and malicious traffics. Recently, graph-neural network (GNN) achieved state-of-the-art performance to model the network topology in cybersecurity tasks. However, only a few works exist using GNNs to tackle the intrusion detection problem. Besides, other promising avenues such as applying the attention mechanism are still under-explored.This paper presents two novel graph-based solutions for intrusion detection, the modified E-GraphSAGE and E-ResGAT algorithms, which rely on the established GraphSAGE and graph attention network (GAT), respectively. The key idea is to integrate residual learning into the GNN leveraging the available graph information. Residual connections are added as a strategy to deal with the high class imbalance, aiming at retaining the original information and improving the minority classes' performance. An extensive experimental evaluation on four recent intrusion detection datasets shows the excellent performance of our approaches, especially when predicting minority classes.

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“…GNNs can also be used for network prediction, e.g., delay prediction [21] and traffic prediction [41,53,106]. The better prediction is the basis of proactive management.…”

Section: Wired Networkmentioning

confidence: 99%

“…A framework named Spatio-temporal Graph Convolutional Recurrent Network (SGCRN) is proposed in [41], which combines GCN and GRU and is validated on the network traffic data from four real IP backbone networks. Another framework named Multi-scale Spatial-temporal Graph Neural Network (MSTNN) is proposed for Origin-Destination Traffic Prediction (ODTP) and two real-world datasets are used for evaluation [53]. Inspired by the prediction model DCRNN [94] developed for road traffic, a nonautoregressive graph-based neural network is used in [106] for network traffic prediction and evaluated on the U.S. Department of Energy's dedicated science network.…”

Section: Wired Networkmentioning

confidence: 99%

“…GNN [82] Botnet Detection [75] GNN Approach GCN [88] Communication Delay Estimation [65] GNNs with Semisupervised Learning GCN [88] Delay Prediction [21] Message-passing Neural Networks MPNN [90] Intrusion Detection [18] Alert-GCN GCN [88] MPLS Configuration Analysis [63] DeepMPLS GNN [82] Network Calculus Analysis [47,32,58] DL-assisted Tandem Matching Analysis GNN [82] Network Configuration Feasibility [70] Ensemble GNN Model GN [91] Network Modeling [42] RouteNet MPNN [90] Network Modeling [72] Extended RouteNet MPNN [90] Network Modeling [105] Graph-based DL GN [91], GNN [82] Network Modeling [39] DeepComNet GGS-NN [93] Routing [79] Graph-Query Neural Network GNN [82] Routing and Load Balancing [81] DL-based Distributed Routing GNN [82] Traffic Prediction [41] SGCRN GCN [88] Traffic Prediction [53] MSTNN GAT [89] Traffic Prediction [106] Nonautoregressive…”

Section: Wired Networkmentioning

confidence: 99%

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Graph-based Deep Learning for Communication Networks: A Survey

Jiang

2021

Preprint

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Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different communication networks, e.g. wireless networks, wired networks, and software-defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.

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MSTNN: A Graph Learning Based Method for the Origin-Destination Traffic Prediction

Cited by 6 publications

References 7 publications

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms

Graph-based Deep Learning for Communication Networks: A Survey

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