Abstract
This paper proposes a network security situation analysis and forecasting system to address the limits of current technologies in uncertainty measurement, multi-source data fusion, and long-sequence dependency modeling. The system uses a Genetic Algorithm to optimize the smoothing parameter and the feature weight of the Probability Neural Network to complete the situation assessment. It also uses a self-attention mechanism to enhance the gated recurrent unit’s temporal modeling ability to complete situation prediction. Experiments were conducted on the CIC-IoMT-2024 benchmark dataset, which includes multiple attack types, including DDoS, brute-force, and command-injection attacks, with comparisons against various state-of-the-art algorithms. Experiments show that the system achieves 96.78% accuracy, 95.02% detection rate, and 3.81% false alarm rate in the assessment task. In the prediction task, the mean absolute error stays below 0.0241, the root mean square error stays below 0.0603, and the coefficient of variation stays below 0.085. Compared with various state-of-the-art models, such as the support vector machine integrated with principal component analysis, the proposed integrated system achieves significant improvements in core metrics, including assessment accuracy and prediction error. These results show that the system keeps high precision, high stability, and strong generalization ability in both assessment and prediction. It offers an effective integrated solution to the limitations of current network security situation technologies. More importantly, this work bridges the gap between high-precision real-time assessment and reliable proactive forecasting in a unified framework. The demonstrated capability for accurate early warning of evolving cyber threats provides a practical pathway to building more intelligent, autonomous active defense systems, which are crucial for safeguarding critical infrastructure in the era of IoT and 5G.
References
Feng H, Chen D, Lv H, Lv Z. Game theory in network security for digital twins in industry. Digital Communications and Networks, 2024, 10(4), 1068–1078.
Ogah M D, Essien J, Ogharandukun M, Abdullahi M. Machine learning models for heterogenous network security anomaly detection. Journal of Computer and Communications, 2024, 12(6):38–58.
Sokol P, Staňa R, Gajdoš A, Pekarèík P. Network security situation awareness forecasting based on statistical approach and neural networks. Logic Journal of the IGPL, 2023, 31(2): 352–374.
Khan M J. Zero trust architecture: Redefining network security paradigms in the digital age. World Journal of Advanced Research and Reviews, 2023, 19(3): 105–116.
Li J, Wu Y, Li Y, Zhang Z, Fouad H, Altameem T. A network security prediction method based on attack defense tree. Journal of Nanoelectronics and Optoelectronics, 2023, 18(3), 357–366.
Chen, Z. 2024. Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning. Journal of Cyber Security and Mobility. 13, 04 (Jun. 2024), 775–802. https://doi.org/10.13052/jcsm2245-1439.1349.
Mlakar P, Merše J, Faganeli Pucer J. Ensemble weather forecast post-processing with a flexible probabilistic neural network approach. Quarterly Journal of the Royal Meteorological Society, 2024, 150(764): 4156–4177.
Zha W, Zhang J, Dan Y, Li, Y. A novel wind power prediction method of the lower upper bound evaluation based on GRU. Transactions of the Institute of Measurement & Control, 2025, 47(3):599–609.
Dora V R S, Lakshmi V N. Smart network security using advanced ensemble-DDoS attack detection and hybrid JA-SLOA-linked optimal routing-based mitigation. Australian journal of electrical and electronics engineering, 2024,21(4):374–396.
Sun J, Li C, Song Y, Ni P, Wang J. Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO. Tech Science Press, 2023, 47(10): 993–1012.
Guo X, Yang J, Gang Z Y A. Research on Network Security Situation Awareness and Dynamic Game Based on Deep Q Learning Network. Journal of Internet Technology, 2023, 24(2):549–563.
Ya-Xing W U, Zhao D M. Build IPSO-ABiLSTM Model for Network Security Situation Prediction. Journal of Information Science & Engineering, 2024, 40(1): 71–88.
Alharbi F, Kashyap G S. Empowering Network Security through Advanced Analysis of Malware Samples: Leveraging System Metrics and Network Log Data for Informed Decision-Making. International Journal of Networked and Distributed Computing, 2024, 12(2): 250–264.
Deshmukh S, Gupta P. Application of probabilistic neural network for speech emotion recognition. International Journal of Speech Technology, 2024, 27(1): 19–28.
Shakir U, Ali A, Hussain M, Radwan A E, Aal A A E. PNN Enhanced Seismic Inversion for Porosity Modeling and Delineating the Potential Heterogeneous Gas Sands via Comparative Inversion Analysis in the Lower Indus Basin. Pure and Applied Geophysics, 2024, 181(9): 2801–2821.
Kadum J, Al-Tuwaijar J M, Kadum A, Rasheed S H. Towards Smart Farming Approach for Plants Disease Detection and Classification by Using HoG Feature Extraction and PNN Algorithm. Mustansiriyah Journal of Pure and Applied Sciences, 2025, 3(1):64–81.
Mohsen S. Recognition of human activity using GRU deep learning algorithm. Multimedia Tools and Applications, 2023, 82(30): 47733–47749.
Cao B, Shang H, Fan M, Sun F. CNN-GRU based method for peak location of reflected Terahertz signals from thermal barrier coatings. Nondestructive Testing and Evaluation, 2024, 39(8): 2132–2149.
Chang Z H. Analysis of Collaborative Characteristics of Reinforcement Learning Intelligent Control and Bayesian Network Model in Network Security Protection. Journal of Cyber Security and Mobility, 2025, 14(2): 365–390.
Zhang L, Liu Y. Network Security Prediction and Situational Assessment Using Neural Network-based Method. Journal of Cyber Security and Mobility, 2023, 12(4): 547–568.
Gao H, Guo L. Research on Network Security Situation Prediction Algorithm Combining Intuitionistic Fuzzy Sets and Deep Neural Networks. SAE International Journal of Connected and Automated Vehicles, 2024, 7(3):341–353.
Shi Y, Liu G, Yang B, Chen Y, Liang Z. A feature selection algorithm for PNN optimized by binary PSO and applied to smart city intrusion detection system. IC-ITECHS, 2024, 5(1), 33–42.
Javeed M D, Nagaraju R, Chandrasekaran R, Rajulu G, Tumuluru P, Ramesh M, Shrivastava R. Brain tumor segmentation and classification with hybrid clustering, probabilistic neural networks. Journal of Intelligent & Fuzzy Systems, 2023, 45(4): 6485–6500.
Li Z. Retracted: Neural network economic forecast method based on genetic algorithm. IET software, 2023, 17(4): 681–693.
Khatri K A, Shah K B, Logeshwaran J, Shrestha A. Genetic algorithm based techno-economic optimization of an isolated hybrid energy system. CRF, 2023, 8(4), 1447–1450.
Yan Y, Zhang W, Liu Y, Li Z. Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction. Journal of Hydroinformatics, 2023, 25(4), 1358–1379.
Xue Q, Fang F. Study on Nowcasting Method of Severe Convective Weather Based on SA-PredRNN++. Journal of Data Science and Intelligent Systems, 2024, 3(3), 176–183.
Uluocak I, Bilgili M. Daily air temperature forecasting using LSTM-CNN and GRU-CNN models. Acta Geophysica, 2024, 72(3): 2107–2126.
Chen J, Liu M, Liu W, Chi D, Xie J, Liu J, Zhang W. Middle-and Long-Term Runoff Forecast Model for Water Resource and Climate Security Based on Self-Attention Mechanism. Land Degradation & Development, 2025, 36(8), 2773–2785.
Chen C, Quan W, Shao Z. Aerial target threat assessment based on gated recurrent unit and self-attention mechanism. Journal of Systems Engineering and Electronics, 2024, 35(2): 361–373.
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