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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Privacy-Preserving Federated Learning based Deep Learning Model for Malicious Behaviour Detection in Wireless Sensor Networks |
| Authors Name: | Tasgheer Ehsan , Kanika Sharma |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206817
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| Published Paper Id: | IJRTI2510102 |
| Published In: | Volume 10 Issue 10, October-2025 |
| DOI: | |
| Abstract: | Malicious behaviour detection in Wireless Sensor Networks (WSNs) is essential because of the increasing number of security threats and the resource-constrained nature of these networks. Standard detection systems often face limitations such as poor scalability, limited accuracy, and inability to adapt to varying data distributions across clients. To address these issues, the proposed method is developed to improve performance and data security. Initially, the Dilated Stacked Convolutional Cross-Attention based Bidirectional Gated Recurrent Unit (DSC2-Bi-GRU) technique is used to detect malicious behaviour in WSNs by integrating Dilated Stacked Convolution (DSC), cross-attention, and Bidirectional Gated Recurrent Unit (Bi-GRU). Federated Learning (FL) is used for decentralized training among dispersed sensor nodes (SNs), enhancing data integrity and reducing risks of centralized data breaches. SNs collaborate to update a global model without exchanging raw data, dramatically boosting privacy and scalability. It is enhanced by using Differential Privacy Stochastic Gradient (DpSG) and Adaptive Elliptic Curve Cryptography (AECC) to ensure privacy and secure communication. Here, DpSG is used to include noise during the aggregation process to ensure privacy. Then, the AECC is used to secure the data by encryption. This proposed model captures spatio-temporal features, which enables accurate detection of intrusion while preserving data confidentiality, scalability, and resilience in resource-constrained and adversarial WSN environments. It is analyzed with other existing techniques to determine its superiority. The proposed approach achieved 0.9989% accuracy, 0.9986% precision, 0.9983% Recall, and 0.9949% F1-score. |
| Keywords: | Wireless Sensor Networks (WSNs), Bidirectional Gated Recurrent Unit, Federated Learning, Cross-Attention, ECC, Differential Privacy, and Data Confidentiality. |
| Cite Article: | "Privacy-Preserving Federated Learning based Deep Learning Model for Malicious Behaviour Detection in Wireless Sensor Networks", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 10, page no.b12-b25, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510102.pdf |
| Downloads: | 000124 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2510102
Registration ID:206817
Published In: Volume 10 Issue 10, October-2025
DOI (Digital Object Identifier):
Page No: b12-b25 Country: Chandigarh, Tamil Nadu, India Research Area: Communication Systems Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2510102 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2510102 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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