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- Title
RFECV And Boruta: Advancing Intrusion Detection In Iot And Smart City Networks.
- Authors
Merlin, R. Tino; Ravi, R.
- Abstract
As IoT devices become increasingly common in smart cities, securing these interconnected networks has become essential. This study proposes an extensive technique for improving intrusion detection systems designed especially for IoT networks in smart city contexts. Leveraging machine learning techniques and preprocessing methodologies, the study addresses the challenge of detecting and mitigating potential security threats. The methodology encompasses various preprocessing steps, including Synthetic Minority Oversampling Technique (SMOTE) employed for class imbalances and Min-Max normalization for scaling feature values. Additionally, advanced feature selection techniques such as Recursive Feature Elimination with Cross-Validation (RFECV) and the Boruta algorithm are employed to identify the most relevant subset of features for accurate intrusion detection. The accuracy achieved by the proposed methodology is 97.62%.
- Subjects
SMART cities; SYSTEMS design; FEATURE selection; MACHINE learning; INTERNET of things
- Publication
Journal of Namibian Studies, 2023, Vol 38, p2015
- ISSN
1863-5954
- Publication type
Article