Construction and Analysis of QPSO-LSTM Model in Network Security Situation Prediction
DOI:
https://doi.org/10.13052/jcsm2245-1439.1334Keywords:
Network security, QPSO algorithm, LSTM neural network, model construction, situation predictionAbstract
The continuous improvement of artificial intelligence technology has deepened its application in many fields and provided more support for predicting network security situations. QPSO-LSTM model based on LSTM neural network and fused with QPSO algorithm provides more options for improving network security situation prediction, further enhancing the effectiveness of network security situation prediction, and enabling more efficient and accurate prediction and analysis of network security situations. By comparing the applications of different types of algorithms in network security situation prediction, it was found that the QPSO-LSTM model has smaller prediction errors, can achieve higher prediction accuracy, and can also obtain higher F1-score and AUC values; the shorter identifying runtime also lays the foundation for improving the speed and efficiency of network security situation prediction. Therefore, in the field of network security situation prediction, the application of QPSO-LSTM model can provide more support for further improvement and improvement of security situation prediction performance in this field.
Downloads
References
Li X. Research on the Impact of Internet Economy on International Economics and Trade[J]. Information Systems and Economics, 2022, 3(3):124–139.
Dongmei L, Jie C, Zilong Y, et al. Prediction Methods for Energy Internet Security Situation Based on Hybrid Neural Network[J]. IOP Conference Series: Earth and Environmental Science, 2021, 645(1): 12–26.
Hongwu Z, Kai K, Wei B. Hierarchical network security situation awareness data fusion method in cloud computing environment[J]. Journal of Computational Methods in Sciences and Engineering, 2023, 23(1):237–251.
Zhicheng W, Longxin Z, Qinlan W, et al. A Network Security Situation Awareness Method Based on GRU in Big Data Environment[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2023, 37(1):126–138.
Zhang L, Liu Y. Network Security Prediction and Situational Assessment Using Neural Network-based Method[J]. Journal of Cyber Security and Mobility, 2023, 12(04), 547–568.
Ying Z, Guodong Z, Roobaea A, et al. Research on data mining method of network security situation awareness based on cloud computing[J]. Journal of Intelligent Systems, 2022, 31(1):520–531.
Guanling Z, Lisheng H, Lu L, et al. Prediction of Industrial Network Security Situation Based on Noise Reduction Using EMD[J]. Mobile Information Systems, 2022.
Xue X, Zheng Y, Lu C. Wireless Network Safety Status Prediction Based on Fuzzy Logic. Journal of Cyber Security and Mobility[J], 2023, 12(04), 589–604.
Li, X. Construction of a Smart City Network Information Security Evaluation Model Based on GRA-BPNN. Journal of Cyber Security and Mobility[J]. 2023, 11(06), 755–776.
Pooja T, Purohit S. Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security[J]. Global Transitions Proceedings, 2021, 2(2):448–454.
Priyanka J, Ramakrishnan M. Security Establishment in Cybersecurity Environment Using PSO Based Optimization[J]. Wireless Personal Communications, 2023, 129(3):1807–1828.
Oluwaseun R O, Bamidele J A, Peter S, et al. An Enhanced Intrusion Detection System using Particle Swarm Optimization Feature Extraction Technique[J]. Procedia Computer Science, 2021, 193(1):504–512.
Pavani M, Rao T P. Adaptive PSO with optimised firefly algorithms for secure cluster-based routing in wireless sensor networks[J]. IET Wireless Sensor Systems, 2019, 9(5):274–283.
Gang W. Comparative study on different neural networks for network security situation prediction[J]. Security and Privacy, 2020, 4(1):24–39.
Yuan Y, Yuangang L. A Modified Hybrid Method Based on PSO, GA, and K-Means for Network Anomaly Detection[J]. Mathematical Problems in Engineering, 2022.
Shandilya, S. K. Design and Deployment of Network Testbed for Web Data Security. Journal of Cyber Security and Mobility[J]. 2021, 11(02), 127–140.
Qinghui L, Tianping Z. Deep learning technology of computer network security detection based on artificial intelligence[J]. Computers and Electrical Engineering, 2023, 110.
Odumuyiwa, V., Alabi, R. DDOS Detection on Internet of Things Using Unsupervised Algorithms. Journal of Cyber Security and Mobility[J]. 2021, 10(3), 569–592.
Guiwen J. Security Detection Design for Laboratory Networks Based on Enhanced LSTM and AdamW Algorithms[J]. International Journal of Information Technologies and Systems Approach (IJITSA), 2023, 16(2):1–13.
Youfeng N, Mingxi G, Wenhao Y, et al. A Bayesian optimization-based LSTM model for DGA domain name identification approach[J]. Journal of Physics: Conference Series, 2022, 2303(1):546–569.
Su, Yishan, Xia, et al. Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network[J]. Neural Processing Letters, 2021, 53(6):1–17.
Li S, Zhao D, Li Q. A Framework for Predicting Network Security Situation Based on the Improved LSTM[J]. EAI Endorsed Transactions on Collaborative Computing, 2020, 4(13):56–72.
Mostofa A, Rahul G, Minhaz M C, et al. Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector[J]. Journal of Cybersecurity and Privacy, 2021, 1(1):199–218.
Panagiotis A P, Lazaros I, Antonios P, et al. COREM2 project: a beginning to end approach for cyber intrusion detection[J]. Neural Computing and Applications, 2022, 34(22):1965–1984.
B V, Preethi G. Network Security Based on Multi-objective Ant Colony Optimization Algorithm[J]. Journal of Research in Science and Engineering, 2021, 3(9):23–36.
Zhao D, Liu J. Study on network security situation awareness based on particle swarm optimization algorithm[J]. Computers & Industrial Engineering, 2018, 125(6):764–775.
Zeng J. Research on secure encryption method of multi-domain fiber network based on particle swarm optimization algorithm[J]. Journal of Intelligent & Fuzzy Systems, 2019, 38(1):139–145.
Shabbir J A, Faisal B, Naseer K Q, et al. Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine[J]. Computers and Electrical Engineering, 2021, 92.
Oluwaseun R O, Bamidele J A, Peter S, et al. An Enhanced Intrusion Detection System using Particle Swarm Optimization Feature Extraction Technique[J]. Procedia Computer Science, 2021, 193(2):504–512.
Wang H, Ruan J, Ma Z, et al. Deep learning aided interval state prediction for improving cyber security in energy internet[J]. Energy, 2019, 174(6):1292–1304.
Davoud Sedighizadeh, Ellips Masehian, Mostafa Sedighizadeh et al. GEPSO: A new generalized particle swarm optimization algorithm[J]. Mathematics and Computers in Simulation, 2021, 179:194–212.
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.
