Construction and Application of Internet of Things Network Security Situation Prediction Model Based on BiLSTM Algorithm
DOI:
https://doi.org/10.13052/jcsm2245-1439.1352Keywords:
IIoT, Situation Assessment, Situation Forecast, Two-way Long-term and Short-term Memory NetworkAbstract
IIoT is more and more extensive. However, security problem of IIoT is increasing. Traditional network security strategies can not fully evaluate the security situation of IIoT. In view of the incomplete selection of situation elements and the single dimension of evaluation system, we selected 14 secondary indicators from four dimensions: operation dimension, fragility dimension, stability dimension and threat dimension, and constructed the evaluation index system of IIoT. In the experiment, we selected 50 enterprises as samples, measured the IIoT system and collected data, and determined weight of each index. This article proposes an improved arithmetic optimization algorithm. Evaluate the performance of the model using a 10x cross validation method. The results show that our model reaches 92% accuracy, which is higher than existing models. Optimize parameters of the BiLSTM network by improving the sparrow search algorithm. The experimental results show that the optimized model also outperforms existing models in prediction accuracy. The MSE and MAE of our model are 0.023 and 0.018, respectively, which are reduced by 30% and 25% compared to existing models.
Downloads
References
Dong, Z., Su, X., Sun, L., and Xu, K. (2021). Network security situation prediction method based on strengthened lstm neural network. Journal of Physics: Conference Series, 1856(1), 012056 (7pp).
Zhang, W., Bai, T. S., and Sun, F. (2019). A method for network security situation prediction based on lstm. Proceedings of the 29th European Safety and Reliability Conference (ESREL).
Yonghao, W., and Cong, L. (2018). Intelligent Substation Network Security Situation Prediction Model Based on Gibbs-LDA. International Conference on Intelligent Computing, Communication and Devices.
Chen, L., Fan, G., Guo, K., and Zhao, J. (2020). Security Situation Prediction of Network Based on Lstm Neural Network. IFIP WG 10.3 International Conference on Network and Parallel Computing. Springer, Cham.
Chen, L., Fan, G., Guo, K., and Zhao, J. (2021). Security Situation Prediction of Network Based on LSTM Neural Network.
Hong, X. (2020). Network security situation prediction based on grey relational analysis and support vector machine algorithm. Int. J. Netw. Secur., 22, 177–182.
Ding, C., Chen, Y., Algarni, A. M., Zhang, G., and Peng, H. (2022). Application of fractal neural network in network security situation awareness. Fractals, 30.
Bian, S., Wang, Z., Song, W., and Zhou, X. (2023). Feature extraction and classification of time-varying power load characteristics based on pcanet and cnn+bi-lstm algorithms. Electric Power Systems Research, 217, 109149.
Liao, H. M., Li, L. L., Xuan, J. X., and Wang, H. N. (2020). Application of Cryptographic Technology Based on Certificateless System in Electricity Internet of Things. 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE).
Zhang, H., Kang, C., and Xiao, Y. (2021). Research on network security situation awareness based on the lstm-dt model. Sensors, 21(14), 4788.
Xiang-Hao, C., and Zhen, L. (2019). Research on sql injection attack detection based on lstm neural network. Journal of Tianjin University of Technology.
Albahrani, E. A., Lafta, S. H., and Ghayad, N. H. (2023). A Chaos-Based Encryption Algorithm for Database System. Journal of Cyber Security and Mobility, 12(01), 25–54.
Yang, H., Zeng, R., Wang, F., Xu, G., and Zhang, J. (2020). An unsupervised learning-based network threat situation assessment model for internet of things. Security and Communication Networks, 2020(9), 1–11.
Dong, M., Zhao, J., Li, D. A., Zhu, B., An, S., and Liu, Z. (2021). Isee: IIoT perception in solar cell detection based on edge computing:. International Journal of Distributed Sensor Networks, 17(11), 21–856.
Li, J., Zhi, J., Hu, W., Wang, L., Yang, A. (2020). Research on the improvement of vision target tracking algorithm for internet of things technology and simple extended application in pellet ore phase. Future Generation Computer Systems, 110, 233–242.
Zhang, B., Hu, W., Ghias, A. M. Y. M., Xu, X., Chen, Z., and Yan, J. (2023). Two-timescale autonomous energy management strategy based on multi-agent deep reinforcement learning approach for residential multicarrier energy system.
Huang, Z., and Liang, Y. (2019). Research of data mining and web technology in university discipline construction decision support system based on mvc model. Library Hi Tech.
Zang, Z. (2022). Analysis of financial management and decision-making in institution of higher learning based on deep learning algorithm. Mobile Information Systems.
Kollipara, V. N. H., Kalakota, S. K., Chamarthi, S., Ramani, S., Malik, P., and Karuppiah, M. (2023). Timestamp Based OTP and Enhanced RSA Key Exchange Scheme with SIT Encryption to Secure IoT Devices. Journal of Cyber Security and Mobility, 12(01), 77–102.
Prasanna, K. S. L., and Challa, N. P. (2023). Deep bi-lstm with binary harris hawkes algorithm-based heart risk level prediction. SN Computer Science, 5(1).
Liu, D., Cheng, J., Yuan, Z., Wang, C., and Niu, H. (2021). Prediction methods for energy internet security situation based on hybrid neural network. IOP Conference Series Earth and Environmental Science, 645, 012085.
Lin, Z., Yu, J., and Liu, S. (2021). The prediction of network security situation based on deep learning method. International Journal of Information and Computer Security, 15(4), 386.
Shang, L., Zhao, W., Zhang, J., Fu, Q., and Yang, Y. (2019). Network Security Situation Prediction Based on Long Short-Term Memory Network. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).
Lv, Y., Ren, H., Gao, X., Sun, T., and Guo, X. (2020). Multi-scale Risk Assessment Model of Network Security Based on LSTM.
Li, S., Zhao, D., and Li, Q. (2020). A framework for predicting network security situation based on the improved lstm. EAI Endorsed Transactions on Collaborative Computing, 4(13), 165278.
Zhao, W., Yang, H., Li, J., Shang, L., Hu, L., and Fu, Q. (2021). Network traffic prediction in network security based on EMD and LSTM.
Tang, X., Chen, M., Cheng, J., Xu, J., and Li, H. (2019). A Security Situation Assessment Method Based on Neural Network. International Symposium on Cyberspace Safety and Security. Springer, Cham.
Baccari, S., Hadded, M., Touati, H., & Muhlethaler, P. (2021). A Secure Trust-aware Cross-layer Routing Protocol for Vehicular Ad hoc Networks. Journal of Cyber Security and Mobility, 10(2), 377–402.
Ashawa, M., Douglas, O., Osamor, J., and Jackie, R. (2022). Improving cloud efficiency through optimized resource allocation technique for load balancing using lstm machine learning algorithm. Journal of Cloud Computing, 11(1), 1–17.
Yan, W., Qiao, L., Krishnapriya, S., and Neware, R. (2022). Research on prediction of school computer network security situation based on iot. International Journal of System Assurance Engineering and Management, 13.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Cyber Security and Mobility
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
