IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset
Abstract The effectiveness of machine learning models can be significantly averse to redundant and irrelevant features present in the large dataset which can cause drastic performance degradation. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalie...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-02-01
|
| Series: | Journal of Big Data |
| Online Access: | https://doi.org/10.1186/s40537-023-00694-8 |
| _version_ | 1811165817658671104 |
|---|---|
| author | Yuhua Yin Julian Jang-Jaccard Wen Xu Amardeep Singh Jinting Zhu Fariza Sabrina Jin Kwak |
| author_facet | Yuhua Yin Julian Jang-Jaccard Wen Xu Amardeep Singh Jinting Zhu Fariza Sabrina Jin Kwak |
| author_sort | Yuhua Yin |
| collection | DOAJ |
| description | Abstract The effectiveness of machine learning models can be significantly averse to redundant and irrelevant features present in the large dataset which can cause drastic performance degradation. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalies using a multilayer perceptron (MLP) network. IGRF-RFE exploits the qualities of both a filter method for its speed and a wrapper method for its relevance search. In the first phase of our approach, we use a combination of two filter methods, information gain (IG) and random forest (RF) respectively, to reduce the feature subset search space. By combining these two filter methods, the influence of less important features but with the high-frequency values selected by IG is more effectively managed by RF resulting in more relevant features to be included in the feature subset search space. In the second phase of our approach, we use a machine learning-based wrapper method that provides a recursive feature elimination (RFE) to further reduce feature dimensions while taking into account the relevance of similar features. Our experimental results obtained based on the UNSW-NB15 dataset confirmed that our proposed method can improve the accuracy of anomaly detection as it can select more relevant features while reducing the feature space. The results show that the feature is reduced from 42 to 23 while the multi-classification accuracy of MLP is improved from 82.25% to 84.24%. |
| first_indexed | 2024-04-10T15:43:50Z |
| format | Article |
| id | doaj.art-1746c8593838417e8c0e867ecaf08e14 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-10T15:43:50Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-1746c8593838417e8c0e867ecaf08e142023-02-12T12:14:33ZengSpringerOpenJournal of Big Data2196-11152023-02-0110112610.1186/s40537-023-00694-8IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 datasetYuhua Yin0Julian Jang-Jaccard1Wen Xu2Amardeep Singh3Jinting Zhu4Fariza Sabrina5Jin Kwak6Comp Sci/Info Tech, Cybersecurity Lab, Massey UniversityComp Sci/Info Tech, Cybersecurity Lab, Massey UniversityComp Sci/Info Tech, Cybersecurity Lab, Massey UniversityComp Sci/Info Tech, Cybersecurity Lab, Massey UniversityComp Sci/Info Tech, Cybersecurity Lab, Massey UniversitySchool of Engineering and Technology, Central Queensland UniversityDepartment of Cyber Security, Ajou UniversityAbstract The effectiveness of machine learning models can be significantly averse to redundant and irrelevant features present in the large dataset which can cause drastic performance degradation. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalies using a multilayer perceptron (MLP) network. IGRF-RFE exploits the qualities of both a filter method for its speed and a wrapper method for its relevance search. In the first phase of our approach, we use a combination of two filter methods, information gain (IG) and random forest (RF) respectively, to reduce the feature subset search space. By combining these two filter methods, the influence of less important features but with the high-frequency values selected by IG is more effectively managed by RF resulting in more relevant features to be included in the feature subset search space. In the second phase of our approach, we use a machine learning-based wrapper method that provides a recursive feature elimination (RFE) to further reduce feature dimensions while taking into account the relevance of similar features. Our experimental results obtained based on the UNSW-NB15 dataset confirmed that our proposed method can improve the accuracy of anomaly detection as it can select more relevant features while reducing the feature space. The results show that the feature is reduced from 42 to 23 while the multi-classification accuracy of MLP is improved from 82.25% to 84.24%.https://doi.org/10.1186/s40537-023-00694-8 |
| spellingShingle | Yuhua Yin Julian Jang-Jaccard Wen Xu Amardeep Singh Jinting Zhu Fariza Sabrina Jin Kwak IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset Journal of Big Data |
| title | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset |
| title_full | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset |
| title_fullStr | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset |
| title_full_unstemmed | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset |
| title_short | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset |
| title_sort | igrf rfe a hybrid feature selection method for mlp based network intrusion detection on unsw nb15 dataset |
| url | https://doi.org/10.1186/s40537-023-00694-8 |
| work_keys_str_mv | AT yuhuayin igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT julianjangjaccard igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT wenxu igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT amardeepsingh igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT jintingzhu igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT farizasabrina igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset AT jinkwak igrfrfeahybridfeatureselectionmethodformlpbasednetworkintrusiondetectiononunswnb15dataset |