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Automated Clone Detection in Wireless Sensor Networks Using Ensemble Learning Models with Hybrid Optimization-Based Significant Feature Selection Approach

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 12
Year of Publication : 2025
Authors : P. Kalvikkarasi, K. Selvakumar
10.14445/23488549/IJECE-V12I12P111
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How to Cite?

P. Kalvikkarasi, K. Selvakumar, "Automated Clone Detection in Wireless Sensor Networks Using Ensemble Learning Models with Hybrid Optimization-Based Significant Feature Selection Approach," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 12, pp. 134-146, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P111

Abstract:

Wireless Sensor Networks (WSN) consist of miniature sensor nodes that communicate among themselves via wireless channels, often in an unfriendly environment, and nodes can be carried and defeated. Thus, an enemy may also attack the clones by copying the nodes taken and broadening the breaching areas with the help of clones. Hence, to reduce the losses of clone nodes to the WSNs, it is crucial to detect them as soon as possible. Other types of clone detection systems have been proposed in the recent past for WSNs, bearing in mind the dissimilar types of network structures, such as deployment strategies and types of devices. The Deep Learning (DL) techniques, however, are used to identify and clone nodes in WSN. A Hybrid Optimization Based Feature Learning is presented in this paper regarding Clone Detection Using Ensemble Learning Models (HOFLCD ELM). The project seeks to create and assess an effective clone detection technique in wireless sensor networks to improve network security and integrity. The initial phase of data preprocessing is the min-max normalization approach, which transforms raw data into a usable format for modeling. In the feature subset selection procedure, the proposed HOFLCD-ELM model develops a hybrid optimization process in the form of Lyrebat Algorithm (LYBA) that integrates Lyrebird Optimization Algorithm (LOA) and Bat Algorithm (BA) in order to find the optimal features within a dataset. Subsequently, the system of Deep Belief Network (DBN) model, Convolutional Variational Autoencoder (CVAE) method, and Graph Convolutional Network (GCN) has been implemented to identify and classify clone attacks. Lastly, the optimization process of the Spider Wasp (SWO) model is used to acquire the parameter tuning process in enhancing the classification of the ensemble classifier. The experimental analysis of the HOFLCD-ELM model is done through a benchmark and a dataset. The results of the empirical study showed that the performance of the HOFLCD-ELM method was improved more than that of the current methods.

Keywords:

Clone detection, Wireless Sensor Networks, Spider Wasp Optimization, Hybrid model, Ensemble deep learning.

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