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Securing Wireless Sensor Networks Using Deep Learning-Based Approach for Eliminating Data Modification in Sensor Nodes


Affiliations
1 Department of Computer Applications, Navarasam Arts and Science College, India
2 Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, India
3 Department of Computer Science, PSG College of Arts and Science, India
4 Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, India
5 Department of Chemistry, School of Mathematics and Natural Sciences, Mukuba University, Zambia
     

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Wireless Sensor Networks (WSNs) play a pivotal role in various domains, including environmental monitoring, surveillance, and industrial automation. However, the inherent vulnerabilities in WSNs make them susceptible to various security threats, such as data modification attacks, which can compromise the integrity and reliability of collected sensor data. To address this issue, we propose a novel approach called Residual Recurrent Transfer Learning (RRTL) to enhance the security of WSNs and eliminate data modification in sensor nodes. RRTL leverages the power of deep learning and transfer learning techniques to develop an intelligent and adaptable security framework. The proposed approach trains a deep residual recurrent neural network (RNN) model using a large dataset of normal sensor readings. This model learns the temporal patterns and dependencies in the data, enabling it to identify abnormal sensor readings that might indicate data modification attempts. To evaluate the effectiveness of RRTL, we conducted extensive experiments using a real-world WSN deployment. The results demonstrate that our approach significantly outperforms existing security mechanisms in terms of accuracy, detection rate, and false positive rate. Furthermore, RRTL exhibits robustness against adversarial attacks and dynamic environmental conditions, making it suitable for real-time applications in challenging WSN environments.

Keywords

Securing, Wireless Sensor Networks, Residual Recurrent Transfer Learning, Eliminating, Data Modification, Sensor Nodes.
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  • Securing Wireless Sensor Networks Using Deep Learning-Based Approach for Eliminating Data Modification in Sensor Nodes

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Authors

S. Karthigai
Department of Computer Applications, Navarasam Arts and Science College, India
Sushiladevi B. Vantamuri
Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, India
C. Anupriya
Department of Computer Science, PSG College of Arts and Science, India
Vinod Sesai
Department of Computer Science and Engineering, BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology, India
A. Nicholas Daniel
Department of Chemistry, School of Mathematics and Natural Sciences, Mukuba University, Zambia

Abstract


Wireless Sensor Networks (WSNs) play a pivotal role in various domains, including environmental monitoring, surveillance, and industrial automation. However, the inherent vulnerabilities in WSNs make them susceptible to various security threats, such as data modification attacks, which can compromise the integrity and reliability of collected sensor data. To address this issue, we propose a novel approach called Residual Recurrent Transfer Learning (RRTL) to enhance the security of WSNs and eliminate data modification in sensor nodes. RRTL leverages the power of deep learning and transfer learning techniques to develop an intelligent and adaptable security framework. The proposed approach trains a deep residual recurrent neural network (RNN) model using a large dataset of normal sensor readings. This model learns the temporal patterns and dependencies in the data, enabling it to identify abnormal sensor readings that might indicate data modification attempts. To evaluate the effectiveness of RRTL, we conducted extensive experiments using a real-world WSN deployment. The results demonstrate that our approach significantly outperforms existing security mechanisms in terms of accuracy, detection rate, and false positive rate. Furthermore, RRTL exhibits robustness against adversarial attacks and dynamic environmental conditions, making it suitable for real-time applications in challenging WSN environments.

Keywords


Securing, Wireless Sensor Networks, Residual Recurrent Transfer Learning, Eliminating, Data Modification, Sensor Nodes.

References