| Autor: |
Gaurav Tripathi, Vishal Krishna Singh, Varun Sharma, Majithia Vivek Vinodbhai |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 20973-20982 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3362794 |
| Popis: |
Network intrusion detection systems work on huge data sets, with large feature sets dominated by noisy data and irrelevant features, resulting in steep degradation in detection accuracy and a steep proliferation in model training and computation time. This work presents a novel method to optimize the feature selection process in machine learning algorithms for accurate detection of intrusion attacks in communication networks. The proposed method targets features with a high impact on the target variable to optimize feature selection and reduction. The CICIDS-2017 data set is used to test the performance of the proposed approach. Results prove the dexterity of the proposed method as it is able to achieve an almost 51% reduction in irrelevant features and increases the detection accuracy of the tuned random forest classifier to 99.9% with an almost 50% reduced model computation time. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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