Research on Security Situation Assessment and Prediction Model of Network System in Deep Learning Environment
DOI:
https://doi.org/10.13052/jcsm2245-1439.1362Keywords:
Situation assessment, Situation prediction, Improved algorithm, Prediction modelAbstract
With the development of the Internet, the network environment is increasingly complex, and the problem of network security is increasingly serious. Traditional passive network security technology has been unable to meet people’s current security needs, in this context, network security situation awareness arises at the historic moment. NSSA technology makes the traditional passive security into active security, from the analysis of unilateral elements to the analysis of the overall security. As key technology of situation perception, security situation assessment and prediction can evaluate and predict the network security situation at the overall level, help network security managers understand the overall network security changes and take protective measures in advance when predicting the dangerous state, which has important research significance. This paper mainly studies the situation assessment and prediction technology of network security, and puts forward the improved model and algorithm, which improves accuracy of situation assessment and prediction results. Prediction accuracy reaches 97.86%, and the efficiency of the situation assessment reaches 98.22%.
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R. Zhang, Z. Pan, Y. Yin, and Z. Cai, “A Model of Network Security Situation Assessment Based on BPNN Optimized by SAA-SSA,” International Journal of Digital Crime and Forensics, vol. 14, no. 2, 2022.
G.-F. Yu, “A multi-objective decision method for the network security situation grade assessment under multi-source information,” Information Fusion, vol. 102, 2024.
D. Zhao, G. Ji, and S. Zeng, “Network security situation assessment based on dual attention mechanism and HHO-ResNeXt,” Connection Science, vol. 35, no. 1, 2023.
R. Zhang, M. Liu, Z. Pan, and Y. Yin, “Network Security Situation Assessment Based on Improved WOA-SVM,” Ieee Access, vol. 10, pp. 96273–96283, 2022.
H. Yang, R. Zeng, G. Xu, and L. Zhang, “A network security situation assessment method based on adversarial deep learning,” Applied Soft Computing, vol. 102, 2021.
H. Wang, D. Zhao, and X. Li, “Research on Network Security Situation Assessment and Forecasting Technology,” Journal of Web Engineering, vol. 19, no. 7–8, pp. 1239–1265, 2020.
L. Yuan, “Prediction of network security situation awareness based on an improved model combined with neural network,” Security and Privacy, vol. 4, no. 6, 2021.
Y. Zhu and Z. Du, “Research on the Key Technologies of Network Security-Oriented Situation Prediction,” Scientific Programming, vol. 2021, 2021.
H. Sun, J. Wang, C. Chen, Z. Li, and J. Li, “ISSA-ELM: A Network Security Situation Prediction Model,” Electronics, vol. 12, no. 1, 2023.
Y. Wang, Y. Yang, R. Gao, S. Li, and Y. Zhao, “A Security Situation Prediction Model for Industrial Control Network Based on EP-CMA-ES,” Ieee Access, vol. 11, pp. 135449–135462, 2023.
D. Zhao, P. Shen, and S. Zeng, “ALSNAP: Attention-based long and short-period network security situation prediction,” Ad Hoc Networks, vol. 150, 2023.
Y.-X. Wu and D.-M. Zhao, “Build IPSO-ABiLSTM Model for Network Security Situation Prediction,” Journal of Information Science and Engineering, vol. 40, no. 1, pp. 71–88, 2024.
S. Duraibi and A. Mujawib Alashjaee, “Enhancing Cyberattack Detection Using Dimensionality Reduction With Hybrid Deep Learning on Internet of Things Environment,” IEEE Access, vol. 12, pp. 84752–84762, 2024.
M. Luan, B. Wang, Y. Zhao, and F. Hu, “Anomalous Subgraph Detection in Given Expected Degree Networks With Deep Learning,” Ieee Access, vol. 9, pp. 60052–60062, 2021.
E. H. Salman, M. A. Taher, Y. I. Hammadi, O. A. Mahmood, A. Muthanna, and A. Koucheryavy, “An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms,” Sensors, vol. 23, no. 1, 2023.
K. Haciefendioglu, F. Mostofi, V. Togan, and H. B. Basaga, “CAM-K: a novel framework for automated estimating pixel area using K-Means algorithm integrated with deep learning based-CAM visualization techniques,” Neural Computing & Applications, vol. 34, no. 20, pp. 17741–17759, 2022.
L. Xiong, J. Liu, B. Song, J. Dang, F. Yang, and H. Lin, “Deep learning compound trend prediction model for hydraulic turbine time series,” International Journal of Low-Carbon Technologies, vol. 16, no. 3, pp. 725–731, 2021.
R. Dong, B. Wang, and K. Cao, “Deep Learning Driven 3D Robust Beamforming for Secure Communication of UAV Systems,” IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1643–1647, 2021.
D. Tian, Y. Han, B. Wang, T. Guan, and W. Wei, “RETRACTED: A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning (Retracted Article),” Computational Intelligence and Neuroscience, vol. 2021, 2021.
G. Nguyen, S. Dlugolinsky, V. Tran, and A. Lopez Garcia, “Deep Learning for Proactive Network Monitoring and Security Protection,” Ieee Access, vol. 8, pp. 19696–19716, 2020.
M. Hamian, K. Faez, S. Nazari, and M. Sabeti, “A novel learning approach in deep spiking neural networks with multi-objective optimization algorithms for automatic digit speech recognition,” Journal of Supercomputing, vol. 79, no. 18, pp. 20263–20288, 2023.
B. Long, Z. Chen, T. Liu, X. Wu, C. He, and L. Wang, “A Novel Medical Image Encryption Scheme Based on Deep Learning Feature Encoding and Decoding,” Ieee Access, vol. 12, pp. 38382–38398, 2024.
L. Almuqren, M. Maray, S. S. Aljameel, R. Allafi, and A. A. Alneil, “Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution,” Electronics, vol. 12, no. 19, 2023.
Z. Guan, P. Zhao, X. Wang, and G. Wang, “Modeling Radio-Frequency Devices Based on Deep Learning Technique,” Electronics, vol. 10, no. 14, 2021.
J. Guan, R. Lai, H. Li, Y. Yang, and L. Gu, “DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping,” Ieee Transactions on Neural Networks and Learning Systems, vol. 34, no. 7, pp. 3255–3268, 2023.
X. Liu, C. Qian, W. Yu, D. Griffith, A. Gopstein, and N. Golmie, “Using Deep Reinforcement Learning to Automate Network Configurations for Internet of Vehicles,” Ieee Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 15948–15958, 2023.
N. G. B. Amma, “A vector convolutional deep autonomous learning classifier for detection of cyber attacks,” Cluster Computing-the Journal of Networks Software Tools and Applications, vol. 25, no. 5, pp. 3447–3458, 2022.
V. Nasir and F. Sassani, “A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges,” International Journal of Advanced Manufacturing Technology, vol. 115, no. 9–10, pp. 2683–2709, 2021.
S. V. Mahadevkar et al., “A Review on Machine Learning Styles in Computer Vision-Techniques and Future Directions,” Ieee Access, vol. 10, pp. 107293–107329, 2022.
J. Cui et al., “Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach,” Ieee Transactions on Parallel and Distributed Systems, vol. 34, no. 9, pp. 2512–2528, 2023.
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