2023 Vol.6 Feb.No1

• Title: A fast method for capturing wireless network intrusion data based on whale swarm optimized random forest
• Name: Zhang Zhi
• Company: (Mingde College of Guizhou University,Guizhou, Guiyang 550025,China)
• Abstract:

  The current conventional wireless network intrusion data capturing method achieves capturing intrusion data by constructing a deep feature extraction model, which leads to poor capturing performance due to the lack of classification of intrusion data. In this regard, a fast capturing method of wireless network intrusion data based on whale swarm optimization random forest is proposed. The random forest algorithm is used to classify the intrusion data, and combined with the whale swarm optimization method to optimally solve the random forest weak classifier weights, construct the data transmission channel model for wireless communication, and realize the fast capture of intrusion data by judging whether the model prediction value exceeds the threshold value. In the experiments, the proposed method is verified for the capture performance. The analysis of the experimental results shows that the algorithm has a high correct rate and excellent capture performance when the proposed method is used to capture the intrusion data.

  
  

• Keyword: whale population optimization; random forest; intrusion data; data capture.
• DOI: 10.12250/jpciams2023090318
• Citation form: Zhang Zhi.A fast method for capturing wireless network intrusion data based on whale swarm optimized random forest [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.79-83

References

[1] Zuppa A F , Ellis D E , Zaoutis T E , et al. Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users [J]. Pharmacoepidemiology and Drug Safety, 2022, 31(4):393-403.

[2] Hja B , Lin M , Ye W , et al. The influence of the neighbourhood environment on peer-to-peer accommodations: a random forest regression analysis [J]. Journal of Hospitality and Tourism Management, 2022, 51:105-118.

[3] Jarmulska B , Bunn D W . Random forest versus logit models: which offers better early warning of fiscal stress?[J]. Journal of Forecasting, 2022, 41(3):455-490.

[4] Mansouri M , Fezai R , Trabelsi M , et al. Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest[J]. IFAC-PapersOnLine, 2022, 55( 6):127-132.

[5] Deng X , Milligan K , Ali-Adeeb R , et al. Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer:[J]. Applied Spectroscopy, 2022, 76(4):462-474.

[6] Raj A , Misra J P , Khanduja D . Modeling of Wire Electro-Spark Machining of Inconel 690 Superalloy Using Support Vector Machine and Random Forest Regression Approaches[J]. Journal of Advanced Manufacturing Systems, 2022, 21(03):557-571.

[7] Maturo F , Verde R . Pooling random forest and functional data analysis for biomedical signals supervised classification: theory and application to electrocardiogram data[J]. Statistics in Medicine, 2022, 41(12):2247-2275.

[8] Merrigan J J , Stone J D , Wagle J P , et al. Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height: A Technical Report[J]. Journal of Strength and Conditioning Research, 2022, 36(1):277-283.

[9] Liang J . Problems and Solutions of Art Professional Service Rural Revitalization Strategy Based on Random Forest Algorithm[J]. Wireless Communications and Mobile Computing, 2022, 2022(1):1-11.

[10] Rezvi A , Moontaha S , Trisha K A , et al. Data mining approach to analyzing intrusion detection of wireless sensor network [J]. Indonesian Journal of Electrical Engineering and Computer Science, 2021, 21(1):516-523.

[11] M.D. Zainlabuddin, Sharma N . Security Enhancement in Data Propagation for Wireless Network [J]. Revista Gestão Inovação e Tecnologias, 2021, 11(4):4110-4119.

[12] Gao B , Bu B , Zhang W , et al. An Intrusion Detection Method Based on Machine Learning and State Observer for Train-Ground Communication Systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, PP(99):1-13.

[13] Jeune L L , Goedeme T , Mentens N . Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework[J]. IEEE Access, 2021, 9:63995-64015.

[14] Hwa B , Fei W B , Lu Z . Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis[J]. Alexandria Engineering Journal, 2021, 60( 5):4689-4699.

[15] Wang X , Cao l , Yang F . Network Intrusion Data Mining Simulation Based on Improved Apriori Algorithm[J]. Computer Simulation,(5)309-312,434.