Power Grid Industrial Control System Traffic Classification Based on Two-Dimensional Convolutional Neural Network

Yue, Gang; sun, Zhuo; Tian, Jianwei; Zhu, Hongyu; Zhang, Bo

doi:10.1007/978-981-19-0390-8_6

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…These fluctuations finally reflect the variances in traffic volume, packet number, and packet interval arrival time (IAT). To enhance the representation capability, ten statistical factors listed in Table 1 are calculated periodically by the way provided in [25]. These factors are sufficient to represent the data exchange at the flow-based and packet-based levels and can be applied in most network engineering tasks.…”

Section: A Tensor Modelling and Low-rank Analysismentioning

confidence: 99%

AG-LRTR: An Adaptive and Generic Low-Rank Tensor-Based Recovery for IIoT Network Traffic Factors Denoising

2022

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The ubiquitous 5G-enable industrial Internet of Things interconnects a great number of intelligent sensors and actors. Network management becomes challenging due to massive traffic data generated by industrial equipment. However, the conventional single traffic factor is insufficient for the increasingly complicated network engineering tasks due to the poor representation capability. Besides, the insecure equipment with open communication access easily brings irregular network fluctuations to network traffic which interferes with the primary traffic factor. The simple and interfered traffic factor decreases the network management efficiency and misleads the operators. Motivated by that, we construct a comprehensive tensor model representing multi-dimension traffic factors to describe the network traffic beneficial characteristics. Meanwhile, an adaptive and generic low-rank tensor recovery (AG-LRTR) algorithm in the tensor singular value decomposition (t-SVD) framework is proposed for denoising. For effective tensor recovery, the alternating direction method of multipliers is employed to theoretically solve the partial augmented Lagrangian function of our objective with a closed-form solution. Numerical experiments on both synthetic data and real-world traffic data in IIoT validate that our proposed algorithm outperforms other state-of-the-art of tensor recovery algorithms.

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Section: A Tensor Modelling and Low-rank Analysismentioning

confidence: 99%