Handling imbalanced data in intrusion detection using time weighted Adaboost support vector machine classifier and crossover boosted Dwarf Mongoose Optimization algorithm
Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Princip...
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| Veröffentlicht in: | Applied soft computing Jg. 167; S. 112327 |
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| Sprache: | Englisch |
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01.12.2024
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| ISSN: | 1568-4946 |
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| Abstract | Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Principal Component Analysis (KPCA) model extracts features presented in the data, and the Crossover Boosted Dwarf Mongoose Optimization (CDMO) algorithm selects the relevant features for classification. The CDMO algorithm offers the advantages of improving exploitation, providing optimal solutions, and balancing global exploitation and local search capabilities. The selected features are classified into five classes using the Time Weighted Adaboost Support Vector Machine (TWASVM) classifier. The TWASVM classifier effectively handles imbalanced data and delivers high-performance results. Experiments conducted on MATLAB R2019a and the proposed model achieved an higher accuracy of 98.6 % and less time complexity of 13 seconds. Comparative analysis demonstrated that the proposed IDS outperforms other state-of-the-art methods. The advantages of CDMO algorithm include improved exploitation, optimal solutions, and a balanced crossover strategy for global exploitation and local search capabilities. The advantages of the TWASVM classifier include the ability to handle imbalanced data and deliver high-performance results. Overall, the proposed IDS offer a novel solution to the challenges of intrusion detection in a rapidly evolving cybersecurity landscape.
•Novel IDS using NSL-KDD dataset with KPCA and CDMO for feature selection.•CDMO enhances feature selection with improved exploitation and optimal solutions.•TWASVM classifier handles imbalanced data with 98.6 % accuracy and low complexity.•MATLAB R2019a experiments show superior performance over state-of-the-art methods.•IDS provides an advanced solution for modern cybersecurity intrusion detection. |
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| AbstractList | Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Principal Component Analysis (KPCA) model extracts features presented in the data, and the Crossover Boosted Dwarf Mongoose Optimization (CDMO) algorithm selects the relevant features for classification. The CDMO algorithm offers the advantages of improving exploitation, providing optimal solutions, and balancing global exploitation and local search capabilities. The selected features are classified into five classes using the Time Weighted Adaboost Support Vector Machine (TWASVM) classifier. The TWASVM classifier effectively handles imbalanced data and delivers high-performance results. Experiments conducted on MATLAB R2019a and the proposed model achieved an higher accuracy of 98.6 % and less time complexity of 13 seconds. Comparative analysis demonstrated that the proposed IDS outperforms other state-of-the-art methods. The advantages of CDMO algorithm include improved exploitation, optimal solutions, and a balanced crossover strategy for global exploitation and local search capabilities. The advantages of the TWASVM classifier include the ability to handle imbalanced data and deliver high-performance results. Overall, the proposed IDS offer a novel solution to the challenges of intrusion detection in a rapidly evolving cybersecurity landscape.
•Novel IDS using NSL-KDD dataset with KPCA and CDMO for feature selection.•CDMO enhances feature selection with improved exploitation and optimal solutions.•TWASVM classifier handles imbalanced data with 98.6 % accuracy and low complexity.•MATLAB R2019a experiments show superior performance over state-of-the-art methods.•IDS provides an advanced solution for modern cybersecurity intrusion detection. |
| ArticleNumber | 112327 |
| Author | Barathi, Bhagavathi Kannu Uma Anu Chandrasekaran, Hemalatha Murugesan, Kanipriya Ramaswamy, Sumathy Mana, Suja Cherukullapurath |
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| Cites_doi | 10.58496/BJN/2023/001 10.3390/math10234565 10.58496/BJML/2024/002 10.26599/BDMA.2020.9020003 10.1111/coin.12433 10.1007/s11042-020-09916-0 10.1007/s10699-019-09589-5 10.1016/j.inffus.2019.07.006 10.1109/ACCESS.2021.3051074 10.1016/j.compeleceng.2022.107876 10.1109/ACCESS.2023.3254915 10.58496/BJML/2023/005 10.1016/j.engappai.2022.104960 10.1109/ACCESS.2021.3116612 10.1007/s11042-020-08724-w 10.1007/s00500-020-05017-0 10.1016/j.iot.2023.100773 10.25046/aj050310 10.58496/BJN/2024/006 10.3390/math10030530 10.1109/ACCESS.2020.3040740 10.1109/COMST.2018.2847722 10.1155/2019/7130868 10.1109/ACCESS.2023.3327016 10.1016/j.cose.2020.102164 |
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| Keywords | Dwarf mongoose AdaBoost Support vector machine and global exploitation Attacks, crossover Intrusion detection Imbalanced Data |
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| SubjectTerms | AdaBoost Attacks, crossover Dwarf mongoose Imbalanced Data Intrusion detection Support vector machine and global exploitation |
| Title | Handling imbalanced data in intrusion detection using time weighted Adaboost support vector machine classifier and crossover boosted Dwarf Mongoose Optimization algorithm |
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