An effective network traffic anomaly detection method based on deep learning for low-orbit satellite

Jiankai Wang; Liming Wang; Zhen Xu

doi:10.1117/12.2679405

16 June 2023 An effective network traffic anomaly detection method based on deep learning for low-orbit satellite

Jiankai Wang, Liming Wang, Zhen Xu

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 127021N (2023) https://doi.org/10.1117/12.2679405
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

With the development of satellite networking technology, low-orbit satellites are gradually facing security threats caused by abnormal traffic. Since some types of attack traffic do not pass through ground stations, we are trying to migrate ground anomaly detection capabilities to satellites. In prior arts, many deep learning methods have been applied to network traffic anomaly detection. However, most of them have deep layers of neural networks and are unsuitable for satellites with limited energy and computing capacity. In this paper, we present SAT-NTAD, an effective low-orbit satellite network traffic anomaly detection method that combines shallow Convolutional Auto-encoder (CAE) and Bidirectional Simple Recurrent Unit (Bi-SRU) to extract the spatial and contextual features of traffic flows, and detects anomalies by utilizing dynamic threshold. Extensive experimental evaluations show that our method can stably maintain the high performance for abnormality detection, which outperforms state-of-the-art approaches, and effectively reduces the computational cost, which is more suitable for the low computing capacity environment of satellites.

Citation Download Citation

Jiankai Wang, Liming Wang, and Zhen Xu "An effective network traffic anomaly detection method based on deep learning for low-orbit satellite", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 127021N (16 June 2023); https://doi.org/10.1117/12.2679405

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KEYWORDS

Satellites

Solid modeling

Feature extraction

Data modeling

Convolution

Deep learning

Neural networks

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