Energy Efficient Intrusion Detection System (IDS) and Feature Selection for IoT using DNN Model

Author(s)	Mr.B.Karthikeyan, Dr.K.Kamali
Country	India
Abstract	To reduce these risks and avoid system paralysis in, it is crucial to create novel methods for establishing strong security layers in Internet of Things (IoT) networks. The low processing power and storage capabilities of the majority of IoT devices mean that current attack protection techniques frequently need to be modified. An energy-efficient Intrusion Detection System (IDS) and feature selection (FS) for IoT using Deep and Convolutional Network (DNN), aims to balance security with the resource constraints of IoT devices. In first phase, Clustered Data Processing technique is applied for the feature selection of dataset. By identifying relationships between input attributes, this technique automatically creates self-organizing models of optimal complexity. In the next phase, DNN based detection model is applied by means of training and testing. Experimental results show that the proposed FS-DNN model outperforms the existing models with respect to accuracy and F1-score metrics
Keywords	Internet of Things (IoT), Intrusion Detection System (IDS), Deep and Convolutional Network (DNN), Feature selection, Clustered Data Processing
Field	Computer Applications
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.36636
Short DOI	https://doi.org/g84xw9

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International Journal For Multidisciplinary Research