ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Shubhanshi Rathore, Rajeev Paulus, A.K. Jaiswal and Aditi Agarwal, “Analysis of QOS and Energy Consumption in IEEE 802.15.4/ZigBee Wireless Sensor Network”, International Journal of Computer Applications, Vol. 121, No. 17, pp. 40-43, 2015.

  • Amritpal Kaur, Jaswinder Kaur and Gurjeevan Singh, “An Efficient Hybrid Topology Construction in Zigbee Sensor Network”, Proceedings of IEEE International Conference on Recent Advances and Innovations in Engineering, pp. 1-6, 2014.

  • W. Osamy, A. A. El-Sawy and A. Salim, “CSOCA: Chicken Swarm Optimization based Clustering Algorithm for Wireless Sensor Networks”, IEEE Access, Vol. 8, pp. 60676-60688, 2020.

  • X. Zhao, H. Zhu, S. Aleksic and Q. Gao, “Energy-Efficient Routing Protocol for Wireless Sensor Networks based on Improved Grey Wolf Optimizer”, KSII Transactions on Internet and Information Systems, Vol. 12, No. 6, pp. 2644-2657, 2018.

  • A. Junpei, B. Leonard, X. Fatos and A. Durresi, “A Cluster Head Selection Method for Wireless Sensor Networks based on Fuzzy Logic”, Proceedings of IEEE International Conference on Region 10, pp. 1-4, 2007.

  • S.A. Sert, A. Alchihabi and A. Yazici, “A Two-Tier Distributed Fuzzy Logic based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks”, IEEE Transactions on Fuzzy Systems, Vol. 26, No. 6, pp. 3615-3629, 2018.

  • P.S. Mehra, M.N. Doja and B. Alam, “Fuzzy based Enhanced Custer Head Selection (FBECS) for WSN”, Journal of King Saud University Science, Vol. 89, No. 1, pp. 1-15, 2018.

  • Syed Muhammad Sajjada, Safdar Hussain Boukb and Muhammad Yousafa, “Neighbor Node Trust Based Intrusion Detection System for WSN”, Proceedings of International Conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 183-188, 2015.

  • Xinying Yu, Fengyin Li, Tao Li, Nan Wu, Hua Wang and Huiyu Zhou, “Trust‑Based Secure Directed Diffusion Routing protocol in WSN”, Journal of Ambient Intelligence and Humanized Computing, Vol. 43, pp. 1-13, 2020.

  • Jitendra Kurmi, Ram Singar Verma and Sarita Soni, “An Efficient and Reliable Methodology for Wormhole Attack Detection in Wireless Sensor Network”, Advances in Computational Sciences and Technology, Vol. 10, No. 5, pp. 1129-1138, 2017.

  • I. Berin Jeba Jingle and J. Jeya A. Celin, “Mining Optimized Positive and Negative Association Rule using Advance ABC Algorithm”, Journal of Theoretical and Applied Information Technology, Vol. 95, No. 24, pp. 6846-6855, 2017.

  • Karaboga, Dervis, and Bahriye Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: artificial Bee Colony (ABC) Algorithm”, Journal of Global Optimization, Vol. 39, No. 3, pp: 459-471, 2007.

  • Soobin Lee and Howon Lee, “Energy-Efficient Data Gathering Scheme Based on Broadcast Transmissions in Wireless Sensor Networks”, The Scientific World Journal, Vol. 2013, pp. 1-17, 2013.

  • Shouling Ji, Raheem Beyah and Zhipeng Cai, “Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 13, No. 3, pp. 626-637, 2014.


Abstract Views: 186

PDF Views: 0




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

Abstract Views: 186  |  PDF Views: 0

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