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Research Article |

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

Author(s): R. Tino Merlin* and R. Ravi

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 2

Publisher : FOREX Publication

Published : 30 April 2024

e-ISSN : 2347-470X

Page(s) : 367-374

DOI: https://doi.org/10.37391/IJEER.120206

Abstract

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios. Evaluation on the UNSW-NB15 dataset demonstrates the effectiveness of the proposed approach, with comparative analysis showcasing its superiority over existing techniques.

Keywords: UNSW-NB15 Dataset, Cybersecurity, IoT, Smart Cities, Data Preprocessing, Feature Selection, Ensemble Classification.




R. Tino Merlin*, Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore 641046, Tamil Nadu, India; Email: tinophd@gmail.com

R. Ravi, Anna University Recognized Research Centre, Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, India; Email: fxcsehod@gmail.com

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R. Tino Merlin and R. Ravi (2024), Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection. IJEER 12(2), 367-374. DOI: 10.37391/IJEER.120206.