A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting
A hybrid machine learning is a combination of multiple types of machine learning algorithms for improving the performance of single classifiers. Currently, cyber intrusion detection systems require high-performance methods for classifications because attackers can develop invasive methods and evade...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-04-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2019.1582861 |
| _version_ | 1797684874920853504 |
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| author | Ployphan Sornsuwit Saichon Jaiyen |
| author_facet | Ployphan Sornsuwit Saichon Jaiyen |
| author_sort | Ployphan Sornsuwit |
| collection | DOAJ |
| description | A hybrid machine learning is a combination of multiple types of machine learning algorithms for improving the performance of single classifiers. Currently, cyber intrusion detection systems require high-performance methods for classifications because attackers can develop invasive methods and evade the detection tools. In this paper, the cyber intrusion detection architecture based on new hybrid machine learning is proposed for multiple cyber intrusion detection. In addition, the correlation-based feature selection is adopted for reducing the irrelevant features and the weight vote of adaptive boosting that is adopted to combine multiple classifiers is concentrated. In the experiments, UNB-CICT or network traffic dataset is used for evaluating the performance of the proposed method. The results show that the proposed method can achieve higher efficiency in every attack type detection. Furthermore, the experiments with Phishing website dataset UNSW-NB 15 dataset NSL-KDD dataset and KDD Cup’99 dataset are also conducted, and the results show that the proposed method can produce higher efficiency as well. |
| first_indexed | 2024-03-12T00:36:04Z |
| format | Article |
| id | doaj.art-4f51b052f34344638d6ebab8e99fbd7e |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:04Z |
| publishDate | 2019-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-4f51b052f34344638d6ebab8e99fbd7e2023-09-15T09:33:57ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452019-04-0133546248210.1080/08839514.2019.15828611582861A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive BoostingPloyphan Sornsuwit0Saichon Jaiyen1King Mongkut’s Institute of Technology LadkrabangKing Mongkut’s Institute of Technology LadkrabangA hybrid machine learning is a combination of multiple types of machine learning algorithms for improving the performance of single classifiers. Currently, cyber intrusion detection systems require high-performance methods for classifications because attackers can develop invasive methods and evade the detection tools. In this paper, the cyber intrusion detection architecture based on new hybrid machine learning is proposed for multiple cyber intrusion detection. In addition, the correlation-based feature selection is adopted for reducing the irrelevant features and the weight vote of adaptive boosting that is adopted to combine multiple classifiers is concentrated. In the experiments, UNB-CICT or network traffic dataset is used for evaluating the performance of the proposed method. The results show that the proposed method can achieve higher efficiency in every attack type detection. Furthermore, the experiments with Phishing website dataset UNSW-NB 15 dataset NSL-KDD dataset and KDD Cup’99 dataset are also conducted, and the results show that the proposed method can produce higher efficiency as well.http://dx.doi.org/10.1080/08839514.2019.1582861 |
| spellingShingle | Ployphan Sornsuwit Saichon Jaiyen A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting Applied Artificial Intelligence |
| title | A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting |
| title_full | A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting |
| title_fullStr | A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting |
| title_full_unstemmed | A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting |
| title_short | A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting |
| title_sort | new hybrid machine learning for cybersecurity threat detection based on adaptive boosting |
| url | http://dx.doi.org/10.1080/08839514.2019.1582861 |
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