Cybersecurity Situational Awareness Model using Improved LSTM-Informer

Xin Zhou; Bo Li

doi:10.62051/ijcsit.v2n3.05

Authors

Xin Zhou
Bo Li

DOI:

https://doi.org/10.62051/ijcsit.v2n3.05

Keywords:

Cybersecurity, Situation Prediction, Long Short-Term Memory, Informer, Empirical Mode Decomposition, Lightweight Gradient Boosting Machine

Abstract

address the problem of low prediction accuracy in current network security situational prediction

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References

G. Ke, R.-S. Chen, Y.-C. Chen, and J.-h. Yeh, "Network security situation prediction method based on support vector machine optimized by artificial Bee colony algorithms," Journal of Computers, vol. 32, no. 1, pp. 144-153, 2021.

G. Wang, "Comparative study on different neural networks for network security situation prediction," Security and Privacy, vol. 4, no. 1, p. e138, 2021.

L. Yuan, "Prediction of network security situation awareness based on an improved model combined with neural network," Security and Privacy, vol. 4, no. 6, p. e181, 2021.

Y. Tang, C. Li, and Y. Song, "Network security situation prediction based on improved particle swarm optimization and extreme learning machine," Journal of Computer Applications, vol. 41, no. 3, p. 768, 2021.

L. Chen, G. Fan, K. Guo, and J. Zhao, "Security situation prediction of network based on lstm neural network," in IFIP international conference on network and parallel computing, 2020: Springer, pp. 140-144.

S. Li, D. Zhao, and Q. Li, "A framework for predicting network security situation based on the improved LSTM," EAI Endorsed Transactions on Collaborative Computing, vol. 4, no. 13, 2020.

J. Lin and M. Wei, "Network security situation prediction based on combining 3D-CNNs and Bi-GRUs," International Journal of Performability Engineering, vol. 16, no. 12, p. 1875, 2020.

C. He and J. Zhu, "Security situation prediction method of GRU neural network based on attention mechanism," Systems Engineering and Electronic Technology, vol. 43, no. 1, pp. 258-266, 2021.

Z. Dongmei and L. Zhijian, "Network security situation prediction based on Transformer," J. Journal of Huazhong University of Science and Technology (Natural Science Edition), vol. 50, no. 05, pp. 46-52, 2022.

S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.

H. Zhou et al., "Informer: Beyond efficient transformer for long sequence time-series forecasting," in Proceedings of the AAAI conference on artificial intelligence, 2021, vol. 35, no. 12, pp. 11106-11115.

N. E. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, vol. 454, no. 1971, pp. 903-995, 1998.

G. Ke et al., "Lightgbm: A highly efficient gradient boosting decision tree," Advances in neural information processing systems, vol. 30, 2017.

R. Eberhart and J. Kennedy, "Particle swarm optimization," in Proceedings of the IEEE international conference on neural networks, 1995, vol. 4: Citeseer, pp. 1942-1948.

S. Choudhary and N. Kesswani, "Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT," Procedia Computer Science, vol. 167, pp. 1561-1573, 2020.

Luo Z, "Research on neural network based cyber security posture assessment and prediction techniques," Master, 2018. [Online]. Available: https://kns.cnki.net/kcms2/article/abstract?v=w1Je9LIFm5BHIu9LTLzVC_YbmKGVCMPz4-UGKUgJrCGt7xXcWj5yFFr0WiKMf6wkFbVAjfZAfxj2jlRDsSWI2C9q_c6SQDtuhr375r-e_oV-0QkhbzqzqituvGbKg5P64yZkmJNcS7lA5UWgXcUDBQ==&uniplatform=NZKPT&language=CHS

Z. Guo, J. Zhou, D. Wang, Z. Lv, and W. Yang, "Network intrusion detection method based on transformer neural network model," J Chongqing Univ, vol. 44, no. 11, pp. 81-88, 2021.

CNVD. "Weekly report on network security information from 2016 to 2023 [Weekly report/Online]." https://www.cnvd.org.cn/webinfo/show/8876 (accessed.

Z. Chen, "Research on the Application of Intelligent Learning Algorithms in Network Security Situation Awareness and Prediction Methods," in 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT), 2021: IEEE, pp. 309-311.

J. Hu, D. Ma, C. Liu, Z. Shi, H. Yan, and C. Hu, "Network security situation prediction based on MR-SVM," IEEE Access, vol. 7, pp. 130937-130945, 2019.

Z. Hu, S. Chen, and H. Chen, "Convolutional Neural Network Based Power Information Network Security Situational Awareness Model," in 2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST), 2022: IEEE, pp. 243-247.

T. Pooja and P. Shrinivasacharya, "Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security," Global Transitions Proceedings, vol. 2, no. 2, pp. 448-454, 2021.

A. Sahu et al., "Multi-source multi-domain data fusion for cyberattack detection in power systems," IEEE Access, vol. 9, pp. 119118-119138, 2021.

Y. Sun, L. Hou, Z. Lv, and D. Peng, "Informer-Based Intrusion Detection Method for Network Attack of Integrated Energy System," IEEE Journal of Radio Frequency Identification, vol. 6, pp. 748-752, 2022.

Z. Xiong, Y. Li, J. Chen, and D. Chen, "FusedCNN-LSTM-AttNet: A Neural Network Model for Cyber Security Situation Prediction," in Proceedings of the 2023 International Conference on Communication Network and Machine Learning, 2023, pp. 207-210.

C. Yao, Y. Yang, J. Yang, and K. Yin, "A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM," Mathematical Problems in Engineering, vol. 2022, 2022.

K. Yin, Y. Yang, C. Yao, and J. Yang, "Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model," IEEE Access, vol. 10, pp. 56145-56157, 2022.

H. Zhang, C. Kang, and Y. Xiao, "Research on network security situation awareness based on the LSTM-DT model," Sensors, vol. 21, no. 14, p. 4788, 2021.

S. Zhang, Q. Fu, and D. An, "Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network," IEEE Access, vol. 11, pp. 129507-129535, 2023.

Z. Zhang et al., "Artificial intelligence in cyber security: research advances, challenges, and opportunities," Artificial Intelligence Review, pp. 1-25, 2022.