Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm
To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/714 |
| _version_ | 1797340167713849344 |
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| author | Caiwu Lu Yunxiang Cao Zebin Wang |
| author_facet | Caiwu Lu Yunxiang Cao Zebin Wang |
| author_sort | Caiwu Lu |
| collection | DOAJ |
| description | To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection. |
| first_indexed | 2024-03-08T09:59:08Z |
| format | Article |
| id | doaj.art-b20a608b53da435083bea1e329ad9e14 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:59:08Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b20a608b53da435083bea1e329ad9e142024-01-29T13:43:54ZengMDPI AGApplied Sciences2076-34172024-01-0114271410.3390/app14020714Research on Intrusion Detection Based on an Enhanced Random Forest AlgorithmCaiwu Lu0Yunxiang Cao1Zebin Wang2School of Resource Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Resource Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaTo address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.https://www.mdpi.com/2076-3417/14/2/714machine learningdata dimensionality reductioncost sensitiveRandom Forestintrusion detection |
| spellingShingle | Caiwu Lu Yunxiang Cao Zebin Wang Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm Applied Sciences machine learning data dimensionality reduction cost sensitive Random Forest intrusion detection |
| title | Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm |
| title_full | Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm |
| title_fullStr | Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm |
| title_full_unstemmed | Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm |
| title_short | Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm |
| title_sort | research on intrusion detection based on an enhanced random forest algorithm |
| topic | machine learning data dimensionality reduction cost sensitive Random Forest intrusion detection |
| url | https://www.mdpi.com/2076-3417/14/2/714 |
| work_keys_str_mv | AT caiwulu researchonintrusiondetectionbasedonanenhancedrandomforestalgorithm AT yunxiangcao researchonintrusiondetectionbasedonanenhancedrandomforestalgorithm AT zebinwang researchonintrusiondetectionbasedonanenhancedrandomforestalgorithm |