BLSAE-SNIDS: A Bi-LSTM Sparse Autoencoder Framework for Satellite Network Intrusion Detection

Shi Shuxin2, Han Bing1, Wu Zhongdai1, 3, Han Dezhi2, Wu Huafeng4 and Mei Xiaojun4

  1. State Key Laboratory of Maritime Technology and Safety
    200135 Shanghai, China
    han.bing@coscoshipping.com
  2. College of information Engineering, Shanghai Maritime University
    201306 Shanghai, China
    dezhihan88@sina.com.cn, shishuxin@stu.shmtu.edu.cn
  3. Shanghai Ship and Shipping Research Institute Co.,Ltd.
    200135 Shanghai, China
    wu.zhongdai@coscoshipping.com
  4. Merchant marine college, Shanghai Maritime University
    201306 Shanghai, China
    xjmei94@163.com, hfwu@shmtu.edu.cn

Abstract

Due to disparities in tolerance, resource availability, and acquisition of labeled training data between satellite-terrestrial integrated networks (STINs) and terrestrial networks, the application of traditional terrestrial network intrusion detection techniques to satellite networks poses significant challenges. This paper presents a satellite network intrusion detection system named Bi-LSTM sparse selfencoder (BLSAE-SNIDS) to address this issue. Through the development of an innovative unsupervised training Bi-LSTM stacked self-encoder, BLSAE-SNIDS facilitates feature extraction from satellite network traffic, diminishes dimensionality, considerably reduces training and testing durations, and enhances the attack prediction accuracy of the classifier. To assess the efficacy of the proposed model, we conduct comprehensive experiments utilizing STIN and UNSW-NB15 datasets. The results obtained from the STIN dataset demonstrate that BLSAE-SNIDS achieves 99.99% accuracy with reduced computational and transmission overheads alongside enhanced flexibility. Furthermore, results from the UNSW-NB15 dataset exhibit BLSAE-SNIDS’ proficiency in detecting various network intrusion attacks efficiently. These findings indicate that BLSAE-SNIDS suits general satellite security networks and offers a novel approach to designing security systems for polar satellite networks, thus exhibiting practical utility.

Key words

Satellite-terrestrial integrated networks, LSTM, Automatic encoder, Unsupervised learning, Network security, Deep learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240401041S

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Shuxin, S., Bing, H., Zhongdai, W., Dezhi, H., Huafeng, W., Xiaojun, M.: BLSAE-SNIDS: A Bi-LSTM Sparse Autoencoder Framework for Satellite Network Intrusion Detection. Computer Science and Information Systems, Vol. 21, No. 4, 1389–1410. (2024), https://doi.org/10.2298/CSIS240401041S