Abstract
With the increasing complexity and security threats in cyberspace, network security situation assessment has become a key technology to ensure digital security. This study proposes a hybrid model integrating graph convolutional neural networks and multi-layer perceptrons to address the limitations of traditional methods in capturing the topological associations of complex networks and dynamic threat responses. First, this model uses graph convolutional neural networks to aggregate node neighborhood information and capture topological features. Then, it conducts deep nonlinear feature learning through multi-layer perceptrons. Finally, it screens key information through pooling layers. Finally, the situation level assessment is achieved by the Softmax classifier. Experiments showed that the accuracy rates of the model on the CICIDS and UNSW-NB15 datasets reached 96.5% and 94.3% respectively, and its performance was superior to that of the comparison models. In the simulation and dynamic environment tests, the model evaluation results were stable, with an average evaluation time of only 66.19 ms and a resource utilization rate of 53.87%. The hybrid model constructed in this study effectively overcomes the challenges of feature fusion and classification in complex network environments. It provides a novel solution for efficiently and accurately assessing network security situations and has significant practical application value.
References
Khan M, Ghafoor L. Adversarial machine learning in the context of network security: Challenges and solutions. Journal of Computational Intelligence and Robotics, 2024, 4(1): 51–63.
De Keersmaeker F, Cao Y, Ndonda G K, Sadre R. A survey of public IoT datasets for network security research. IEEE Communications Surveys & Tutorials, 2023, 25(3): 1808–1840.
Su J, Jiang M. A hybrid entropy and blockchain approach for network security defense in SDN-based IIoT. Chinese Journal of Electronics, 2023, 32(3): 531–541.
Guo Y, Zhang S. Research on Construction and Application of Network Security Situational Awareness Platform Based on Big Data. IEIE Transactions on Smart Processing & Computing, 2025, 14(2): 218–228.
Cheng M, Li S, Wang Y, Zhou G, Han P, Zhao Y. A New Model for Network Security Situation Assessment of the Industrial Internet. Computers, Materials & Continua, 2023, 75(2): 31–35.
Sun J, Li C, Song Y. A network security situation prediction approach based on MAML and BiGRU. Journal of Intelligent & Fuzzy Systems, 2024, 47(4): 307–319.
Yu Y. A network security situation assessment method based on fusion model. Discover Applied Sciences, 2024, 6(3): 97–98.
Wang Q, Ren X, Li L, Pen H. Design of Network Security Assessment and Prediction Model Based on Improved K-means Clustering and Intelligent Optimization Recurrent Neural Network. International Journal of Advanced Computer Science & Applications, 2024, 15(6): 17–19.
Xu X, Zhao X, Wei M. A comprehensive review of graph convolutional networks: approaches and applications. Electronic Research Archive, 2023, 31(7): 4185–4215.
Yang H, Li Z, Qi Y. Predicting traffic propagation flow in urban road network with multi-graph convolutional network. Complex & Intelligent Systems, 2024, 10(1): 23–35.
Reka R, Karthick R, Ram R S, Singh G. Multi head self-attention gated graph convolutional network based multi-attack intrusion detection in MANET. Computers & Security, 2024, 136: 103–106.
Presekal A, Ştefanov A, Rajkumar V S, Palensky P. Attack graph model for cyber-physical power systems using hybrid deep learning. IEEE Transactions on Smart Grid, 2023, 14(5): 4007–4020.
Alarab I, Prakoonwit S. Graph-based lstm for anti-money laundering: Experimenting temporal graph convolutional network with bitcoin data. Neural Processing Letters, 2023, 55(1): 689–707.
Shi W, Zhang J. Integration of local position-POS awareness and global dense connection for ABSA. Journal of Experimental & Theoretical Artificial Intelligence, 2025, 37(3): 391–411.
He H, Yu X, Zhang J, Song S, B. Letaief K. Message passing meets graph neural networks: A new paradigm for massive MIMO systems. IEEE Transactions on Wireless Communications, 2023, 23(5): 4709–4723.
Pal S, Roy A, Shivakumara P, Pal U. Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images. Artificial Intelligence and Applications, 2023, 1(3): 145–154.
Raja M W. Artificial Intelligence-Based Healthcare Data Analysis Using Multi-perceptron Neural Network (MPNN) Based On Optimal Feature Selection. SN Computer Science, 2024, 5(8): 1034–1035.
Lu J, Lan J, Huang Y, Song M, Liu X. Anti-attack intrusion detection model based on MPNN and traffic spatiotemporal characteristics. Journal of Grid Computing, 2023, 21(4): 60–65.
Zhang J, Li C, Yin Y, Zhang J, Grzegorzek M. Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artificial Intelligence Review, 2023, 56(2): 1013–1070.
Altay O, Varol Altay E. A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer. Neural Computing and Applications, 2023, 35(1): 529–556.
Q. Wu, “Network Security Maintenance and Detection Based on Diversified Features and Knowledge Graph”, JCSANDM, vol. 14, no. 02, pp. 339–364, Jun. 2025.
K. Somsuk, “Enhanced Algorithm for Recovering RSA Plaintext when Two Modulus Values Share At least One Common Prime Factor”, JCSANDM, vol. 14, no. 02, pp. 433–456, Jun. 2025.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility