Designing A Novel Cyber Resilient System for Smart Home Utilizing Recursive Feature Elimination
Keywords:
IoT Intrusion Datasets, Feature Reduction, Light Gradient Boosting Machine (LGBM).Abstract
The proliferation of Internet of Things (IoT) devices in smart homes has introduced significant security challenges, making advanced intrusion detection systems (IDS) essential for itigating potential threats. In this study, proposed a comprehensive framework for designing and implementing an IDS specifically tailored for IoT-enabled smart homes. The approach integrates LightGBM (LGBM) for attack detection and employs feature reduction techniques to enhance model performance and efficiency. This paper evaluated IDS using both IoT datasets and tested three feature reduction methods: Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). The results show that RFE provided the best balance of detection accuracy and computational efficiency. The model effectively identified Mirai and Gafgyt attacks, achieving high accuracy rates of 99% on the datasets. Compared to previous systems, model demonstrated superior performance with reduced computational overhead, enhancing both detection accuracy and operational efficiency. Privacy and security considerations were carefully addressed throughout the design process to ensure user data protection and compliance with relevant regulations. Future work will focus on expanding the model’s capability to detect a broader range of attacks, improving scalability, and integrating advanced IoT technologies to further refine the IDS framework.
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