Author(s)

RongPeng Yan

Affiliation(s)

Sports Engineering College, Beijing Sport University, Beijing 100091, China.

Corresponding Author

RongPeng Yan

ABSTRACT

With the rapid development of Internet technology, network security problems are becoming increasingly serious, and the frequency and complexity of network attacks are increasing, posing a serious threat to personal privacy, corporate interests and even national security. For the problem of redundant feature interference and dimensional disaster in high-dimensional network traffic data, this paper compares the effectiveness of feature screening and dimensionality reduction techniques, such as ANOVA, chi-square test and PCA, respectively, for the removal of irrelevant features in high-dimensional network traffic data, and the experimental results show that PCA solves the problem of high complexity of high-dimensional data processing and effectively improves the classification performance and operational efficiency of the model. Therefore, this study innovatively proposes a hybrid intrusion detection model that integrates Principal Component Analysis (PCA) and Random Forest (RF), and adopts a grid search algorithm to automate the optimization of the hyper-parameter set of the Random Forest, and finally the model has an accuracy of 99.81% in the test set, which indicates that it performs well in classifying the attack and normal traffic. Overall, the model provides an efficient and accurate solution for network intrusion detection, which has important reference value for future research and practical applications.

KEYWORDS

Principal component analysis; Random forest; Network intrusion detection; Feature selection

CITE THIS PAPER

RongPeng Yan. Network intrusion detection based on random forest algorithm. Journal of Computer Science and Electrical Engineering. 2025, 7(3): 46-53. DOI: https://doi.org/10.61784/jcsee3056.

REFERENCES

[1] Liang Junwei, Yang Geng, Ma Maode, et al. Collaborative intrusion detection system for industrial CPS based on secure federated distillation GAN. Journal of Communications, 2023, 44(12): 230-244.

[2] Zhao Y. Practice of computer network security technology in network security maintenance. China New Communication, 2023, 25(20): 101-103.

[3] He Feifei. Research and implementation of real-time network intrusion detection method based on deep learning. Ningxia University, 2022.

[4] Li B. Strict minimal beacon solving and net state analysis based on Petri net decomposition technique. Xi'an University of Electronic Science and Technology, 2021.

[5] Zhou Y. PCA and KNN feature selection for network intrusion detection. Computer and Network, 2021, 47(22): 48-49.

[6] Zhu L J, Zhao G P, Kang L H. Intrusion detection method based on the combination of MI feature selection and KNN classifier. Gansu Science and Technology, 2022, 38(15): 33-36.

[7] Jianhua Chen, Zhangqian Wu, Wei Song. A particle swarm optimization algorithm incorporating precocity detection mechanism and opposing stochastic wandering strategy. Computer Applications, 2024, 44(S2): 123-128.

[8] Hong Zhou,Yang Gang, Yang Jinsong, et al. Feature selection of tag library with joint filtered and embedded samples. Electronic Design Engineering, 2024, 32(22): 146-150.

[9] Meng Yuru, Ren Xiaoling, Wei Ziyi. A guided filter image fusion method based on the combination of PCA and NSCT. Computer and Digital Engineering, 2024, 52(10): 3116-3120.

[10] Guo Yu-Han, Zhu Ru-Shi. A dynamic point-of-entry recommendation algorithm based on multimodal deep forest and iterative Kuhn-Munkres. Computer Application Research, 2024, 41(12): 3634-3644.