The DOI matches its Crossref record; 3 of the 4 references it cites were found in a registry or an index.
Research on Intrusion Detection Method Based on Pearson Correlation Coefficient Feature Selection Algorithm
Open accessAbstract
The current era is the era of big data and 5G. The network security data in the network is different from the past, and the network security data is growing exponentially. As an important line of defense for network security, intrusion detection technology can efficiently detect and process massive amounts of security data has become an important factor restricting its development. The feature selection method of intrusion detection data directly affects the efficiency of intrusion detection. Therefore, this paper proposes a feature selection algorithm based on pearson correlation coefficient, which performs feature specification on many features, which greatly reduces the amount of security data that needs to be processed, and effectively reduces the dimensionality of the data to increase the intrusion. Detection efficiency.
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“This study adopted a strategy based on correlation analysis to systematically eliminate redundant features [53].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [53]
Citations
Cites
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033
Moustafa, N., & Slay, J. (2016) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2015.1125974 · Cited by 1,191
An application of machine learning to network intrusion detection
Sinclair, C., Pierce, L., & Matzner, S. (2003) · Annual Computer Security Applications Conference · doi:10.1109/csac.1999.816048 · Cited by 278
Research works printed but not linked: 3
- Guang, 2019, Methods for obtaining mutually independent features in network intrusion detection systems, J. Computer Engineering and Applications, 2, 125
- Zhu, 2017, Application of machine learning in network intrusion detection, J. Data Acquisition and Processing, 32, 479
- Xinqian, 2019, Intrusion detection method based on multi-dimensional optimization of traffic anomaly analysis, J. Journal of Information Security, 4, 14
Cited by
Classification Prediction of Breast Cancer Based on Machine Learning
Chen, H., Wang, N., Du, X., Mei, K., Zhou, Y. M., & Cai, G. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/6530719 · Cited by 136
Evaluating the impact of filter-based feature selection in intrusion detection systems
Zouhri, H., Idri, A., & Ratnani, A. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00767-y · Cited by 65
Diagnosis of Parkinson’s disease based on voice signals using SHAP and hard voting ensemble method
Ghaheri, P., Nasiri, H., Shateri, A., & Homafar, A. (2023) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2023.2263125 · Cited by 45
Xiang, F., Zhao, Y.-L., Zhang, M., Zuo, Y. N., Zou, X., & Tao, F. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.03.001 · Cited by 43
Zhang, M., Zhao, J., Li, C., Ge, F., Wu, J., Jiang, B., Song, J., & Song, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac392 · Cited by 33
Metadata
- Authors
- Chen, P., Li, F., & Wu, C.
- Affiliations
- Chengdu University of Information Technology (CN)
- Corresponding author
- Li, Fei
- ORCID
- Venue
- Journal of Physics: Conference Series
- Publisher
- IOP Publishing
- Published
- 2021-01-01
- Volume
- 1757
- Issue
- 1
- Pages
- 012054
- ISSN
- 1742-6588 (ISSN-L)
- 1742-6596
- OpenAlex
- W3126533924
- Cited by
- 62
- References
- 4
- Citations by year
- 2026: 4 · 2025: 23 · 2024: 14 · 2023: 13 · 2022: 7 · 2021: 1
- FWCI
- 8.09
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Physics Conference Series (publishedVersion, cc-by)
- Funders
- Natural Science Foundation of Sichuan Province
- Chengdu Science and Technology Program (-YF05-)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection, feature selection, Pearson correlation, dimensionality reduction, big data, 5G
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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