Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

  • Xuejun Liu Beijing Institute of Petrochemical Technology
  • Kaili Li
  • Wenhui Wang
  • Yong Yan
  • Yun Sha
  • Jianping Chen
  • Jiaojiao Qin


Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection.


[1] XiaoFeng Lu; YuYing Liao; Pietro Lio; Pan Hui. (2020). An Efficient Asynchronous Federated Learning Mechanism for Edge Computing, Journal of Computer Research and Development, 57(12), 2571-2582, 2020.

[2] ShuaiQi Shen; Kuan Zhang; Yi Zhou; Song CI. (2020). Security in edge-assisted Internet of Things:Challenges and Solutions, Science China(Information Sciences), 63(12), 27-40, 2020.

[3] ChengXu Zhang; HongWu Li; Yang Qu; JinWu Wei.(2021). Development and Application of 5G Edge Computing for Industrial Internet, Telecommunications Science, 37(01), 129-136, 2021.

[4] Hui Li; Xiuhua Li; Qingyu Xiong; Junhao Wen; Luxi Cheng; Bin Xing.(2021). Edge Computing Powering the Industrial Internet: Architecture, Applications, and Challenges, Computer Science, 48(01), 1-10, 2021.

[5] WenAn Zhang; Zhen Hong; JunWei Zhu; Chen Bo. (2019). Survey of Network Intrusion Detection Methods in Industrial Control System, Control and Decision Making, 34(11):, 2277-2288, 2019.

[6] ShangHong Zhang; GaoFeng Cui; YaTing Long; WeiDong Wang. (2021). Joint Computing and Communication Resource Allocation for Satellite Communication Networks with Edge Computing, Chinese Communication, 18(07), 236-252, 2021.

[7] WanLi Jia; Yang Liu; LeLe Zhang. (2019). Reservoir Prediction Based on RBF Neural Network Optimized by PCA, Proceedings of 2019 China Geoscience joint annual meeting (17), 143-144, 2019.

[8] Zhen Pang; WeiHong Xu. (2012). A learning method of RBF Neural Network Based on Improved K-Means Method, Computer Engineering and Application, 48(11), 161-163+184, 2012.

[9] Qian Sun; Xin Zhao. (2020). DNA Sequence Classification Based on RBF Neural Network Optimized by Particle Swarm Optimization, Modern Electronic Technology, 43(09), 87-91, 2020.

[10] Rui Yang. (2011). Optimization of RBF Neural Network Controller Based on Genetic Algorithm, Harbin University of Technology, 2011.

[11] Wei Lou; XingGao Liu. (2007). Prediction Model of Melt Index of Polypropylene Based on PCAGA- RBF Network, Journal of Petrochemical Colleges and Universities, 2007(03), 82-85, 2007.

[12] Chang Liu; ZhiGang Li; LiGuo Wei; JiZhong Wang; Yang Li. (2019). Application of RBF Neural Network Based on Ant Colony Algorithm in Impulse Grain Flow Sensor, Jiangsu Agricultural Sciences, 47(15), 259-263, 2019.

[13] Swarna Priya R.M.; Praveen Kumar Reddy Maddikunta; Parimala M.; Srinivas Koppu; Thippa Reddy Gadekallu; Chiranji Lal Chowdhary; Mamoun Alazab. (2020). An Effective Feature Engineering for DNN Using Hybrid PCA-GWO for Intrusion Detection in IOMT Architecture, Computer Communications, 160, 2020.

[14] Chen Decheng; Fu Rong; Song Shaoqun; Qin Jie. (2018). Network Security Situation Awareness of Power Dispatching Automation System Based on LDA-RBF, 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), 23-25, 2018.

[15] ZhiZheng Zhang; DongJie Wang; YongLiang Zhang. (2020). Research on Fault Monitoring and Diagnosis Method Based on Improved KPCA-SVM Based on PSO Method, Modern Manufacturing Engineering, (09), 101-107, 2020.

[16] YongMing Han; ChenYu Fan; ZhiQiang Geng; Bo Ma; Di Cong; Kai Chen; Bin Yu. (2020). Energy Efficient Building Envelope Using Novel RBF Neural Network Integrated Affinity Propagation, Energy, 209, 2020.

[17] Shadi; Abpeykar Mehdi; GhateeHadi; ZareEnsemble. (2019). Decision Forest of RBF Networks via Hybrid Feature Clustering Approach for High-Dimensional Data Classification, Computational Statistics & Data Analysis, 12-36, 2019.

[18] GuiMei Yao; BaoBin Miao. (2019). Ship Course Keeping Control Design Based on Reduced Clustering and Adaptive Neuro Fuzzy Inference, Ship Engineering, 41(04), 82-87+13957, 2019.

[19] Olusegun David Samuel; Modestus O. Okwu; Oluwayomi J. Oyejide; Ebrahim Taghinezhad; Asif Afzal; Mohammad Kaveh. (2020). Optimizing Biodiesel Production from Abundant Waste Oils through Empirical Method and Grey Wolf Optimizer, Fuel, 281, 2020.

[20] Akanksha Bhardwaj; Alpesh Kumar. (2020). Numerical Solution of Time Fractional Tricomi-type Equation by An RBF Based Meshless Method, Engineering Analysis with Boundary Elements, 118, 2020.

[21] Mathematics. (2020). New Findings from Jiangsu University of Science and Technology Describe Advances in Mathematics (Coordinated Control and Dynamic Optimal Dispatch of Islanded Microgrid System Based on GWO), Journal of Mathematics, 20-22, 2020.

[22] Vaclav Skala; Martin Cervenka. (2019). Novel RBF Approximation Method Based on Geometrical Properties for Signal Processing with a New RBF Function: Experimental Comparison, IEEE 15th International Scientific Conference on Informatics, 2019.

[23] Vivek Yadav; Girish Parmar; Rajesh Bhatt. (2019). Robustness Analysis with Perturbation for Control System with GWO, 2019 4th International Conference on Information Systems and Computer Networks (ISCON), 2019.

[24] YouWei Liu; ShaosSheng Fan; Yong Feng; LiJun Tang. (2019). Stockbridge Damper Identification Of Overhead Power Lines Based On HOG Feature And GWO-SVM, 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), 2019.

[25] Alisha Ahmed; Girish Parmar; Rajeev Gupta. (2018). Application of GWO in Design of Fractional Order PID Controller for Control of DC Motor and Robustness Analysis, 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018.

[26] Allan Christian Krainski Ferrari; Gideon Villar Leandro; Leandro dos Santos Coelho; Carlos Alexandre Gouvea da Silva. (2019). Tuning of Control Parameters of Grey Wolf Optimizer using Fuzzy Inference, IEEE Latin America Transactions, 17(07), 1191-1198, 2019.

[27] Xu Liang; Di Wang; Ming Huang. (2019). Improved Grey Wolf Optimizer and Their Applications, 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 2019.

[28] Kartikeya Jaiswal; Himanshu Mittal; Sonia Kukreja. (2017). Randomized Grey Wolf Optimizer (RGWO) with Randomly Weighted Coefficients, 2017 Tenth International Conference on Contemporary Computing (IC3), 2017.

[29] Riccardo Taormina; Nils Ole Tippenhauer; Stefano Galelli; Elad Salomons. (2018). The Battle of The Attack Detection Algorithms: Disclosing Cyber Attacks On Water Distribution Netwoks, Water Resources Planning and Management, 2018.

[30] Siwar Kriaa; Ludovic Pietre-Cambacedes; Marc Bouissou; Yoran Halgand. (2015). A Survey of Approaches Combining Safety and Security for Industrial Control Systems, Reliability Engineering and System Safety, 139(5), 156-178, 2015.

[31] Lin Zhang; YanWen Huang; Jie Xuan; Xiong Fu; QiaoMin Lin; RuChuan Wang. (2021). Trust Evaluation Model Based on PSO and LSTM for Huge Information Environments, Chinese Journal of Electronics, 30(01), 92-101, 2021.

[32] Yue Hu; YaFengWang; HaoChengWang. (2020). A Decoding Method Based on RNN for OvTDM, Chinese Communication, 17(04), 1-10, 2020.

[33] Bo Han; LiNa Qiao; JingLin Chen; XianDa Zhang; YanXia Zhang; YongHeng Zhao. (2021). Genetic KNN: A Weighted KNN Approach Supported by Genetic Algorithm for Photometric Redshift Estimation of Quasars, Research in Astronomy and Astrophysics, 21(01), 167-179, 2021.

[34] Mohammad Hashem Haghighat; Jun Li. (2021). Intrusion Detection System Using Voting-Based Neural Network, Tsinghua Science and Technology, 26(04), 484-495, 2021.
How to Cite
LIU, Xuejun et al. Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 4, july 2021. ISSN 1841-9844. Available at: <>. Date accessed: 17 sep. 2021. doi: