Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems
This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini’s effectiveness within the domain of intrusion detection systems (ID...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119768/ http://psasir.upm.edu.my/id/eprint/119768/1/119768.pdf |
| _version_ | 1848868046165245952 |
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| author | Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Akleylek, Sedat |
| author_facet | Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Akleylek, Sedat |
| author_sort | Bouke, Mohamed Aly |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini’s effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context. BukaGini’s performance is evaluated across four diverse datasets commonly used in IDS research: NSLKDD (22,544 samples), WUSTL EHMS (16,318 samples), WSN-DS (374,661 samples), and UNSWNB15 (175,341 samples), amounting to a total of 588,864 data samples. The evaluation encompasses key metrics such as stability score, accuracy, F1-score, recall, precision, and ROC AUC. Results indicate significant advancements in IDS performance, with BukaGini achieving remarkable accuracy rates of up to 99% and stability scores consistently surpassing 99% across all datasets. Additionally, BukaGini demonstrates an average reduction in dimensionality of 25%, selecting 10 features for each dataset using the Gini index. Through rigorous comparative analysis with existing methodologies, BukaGini emerges as a promising solution for feature interaction analysis within cybersecurity applications, particularly in the context of IDS. These findings highlight the potential of BukaGini to contribute to robust model performance and propel intrusion detection capabilities to new heights in real-world scenarios. |
| first_indexed | 2025-11-15T14:46:09Z |
| format | Article |
| id | upm-119768 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:46:09Z |
| publishDate | 2024 |
| publisher | PeerJ Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1197682025-09-19T07:30:35Z http://psasir.upm.edu.my/id/eprint/119768/ Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Akleylek, Sedat This article presents an evaluation of BukaGini, a stability-aware Gini index feature selection algorithm designed to enhance model performance in machine learning applications. Specifically, the study focuses on assessing BukaGini’s effectiveness within the domain of intrusion detection systems (IDS). Recognizing the need for improved feature interaction analysis methodologies in IDS, this research aims to investigate the performance of BukaGini in this context. BukaGini’s performance is evaluated across four diverse datasets commonly used in IDS research: NSLKDD (22,544 samples), WUSTL EHMS (16,318 samples), WSN-DS (374,661 samples), and UNSWNB15 (175,341 samples), amounting to a total of 588,864 data samples. The evaluation encompasses key metrics such as stability score, accuracy, F1-score, recall, precision, and ROC AUC. Results indicate significant advancements in IDS performance, with BukaGini achieving remarkable accuracy rates of up to 99% and stability scores consistently surpassing 99% across all datasets. Additionally, BukaGini demonstrates an average reduction in dimensionality of 25%, selecting 10 features for each dataset using the Gini index. Through rigorous comparative analysis with existing methodologies, BukaGini emerges as a promising solution for feature interaction analysis within cybersecurity applications, particularly in the context of IDS. These findings highlight the potential of BukaGini to contribute to robust model performance and propel intrusion detection capabilities to new heights in real-world scenarios. PeerJ Inc. 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/119768/1/119768.pdf Bouke, Mohamed Aly and Abdullah, Azizol and Cengiz, Korhan and Akleylek, Sedat (2024) Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems. PeerJ Computer Science, 10. pp. 1-26. ISSN 2376-5992 https://peerj.com/articles/cs-2043/ 10.7717/peerj-cs.2043 |
| spellingShingle | Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Akleylek, Sedat Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title | Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title_full | Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title_fullStr | Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title_full_unstemmed | Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title_short | Application of BukaGini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| title_sort | application of bukagini algorithm for enhanced feature interaction analysis in intrusion detection systems |
| url | http://psasir.upm.edu.my/id/eprint/119768/ http://psasir.upm.edu.my/id/eprint/119768/ http://psasir.upm.edu.my/id/eprint/119768/ http://psasir.upm.edu.my/id/eprint/119768/1/119768.pdf |