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

Elimination of Data Modification in Sensor Nodes of WSN Using Deep Learning Model


Affiliations
1 Department of Information Technology, Karpagam Institute of Technology, India
2 Department of Computer Science & Engineering, SNS College of Technology, India
3 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, India
     

   Subscribe/Renew Journal


This study focuses on removing the possibility of malicious data manipulation in wireless sensor networks (WSN) by utilising a deep learning method. When training deep neural networks, datasets that have been the subject of an attack that alters the data are used as the building blocks. This is done in preparation for putting the networks to the test in the real world. We find out through simulation with a 70:30 cross-validation across a 10-fold sample size that the proposed technique is superior to the current state of the art in terms of the packet delivery rate, latency and throughput.

Keywords

Deep Learning Model, Data Modification, WSN, Throughput
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Sundaram, M. Pajic and G.J. Pappas, “The Wireless Control Network: Monitoring for Malicious Behavior”, Proceedings of IEEE Conference on Decision and Control, pp. 5979-5984, 2010.
  • L.M. Abdulrahman and K.H. Sharif, “A State of Art for Smart Gateways Issues and Modification”, Asian Journal of Research in Computer Science, Vol. 63, pp. 1-13, 2021.
  • Z.H. Pang and G.P. Liu, “Detection of Stealthy False Data Injection Attacks against Networked Control Systems via Active Data modification”, Information Sciences, Vol. 546, pp. 192-205, 2021.
  • M. Soni and D.K. Singh, “LAKA: Lightweight Authentication and Key Agreement Protocol for Internet of Things based Wireless Body Area Network”, Wireless Personal Communications, Vol. 122, 1-18, 2021.
  • T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.
  • T.H. Hadi, “Types of Attacks in Wireless Communication Networks”, Webology, Vol. 19, No. 1, pp. 1-13, 2022.
  • R. Rajendran, “An Optimal Strategy to Countermeasure the Impersonation Attack in Wireless Mesh Network”, International Journal of Information Technology, Vol. 13, No. 3, pp. 1033-1038, 2021.
  • X.S. Shen and B. Ying, “Data Management for Future Wireless Networks: Architecture, Privacy Preservation, and Regulation”, IEEE Network, Vol. 35, No. 1, pp. 8-15, 2021.
  • M. Ponnusamy, P. Bedi and T. Suresh, “Design and Analysis of Text Document Clustering using Salp Swarm Algorithm”, The Journal of Supercomputing, Vol. 87, pp. 1-17, 2022.
  • Z. Bin and H. Jian Feng, “Design and Implementation of Incremental Data Capturing in Wireless Network Planning based on Log Mining”, Proceedings of International Conference on Advanced Information Technology, Electronic and Automation Control, pp. 2757-2761, 2021.
  • D. Dilmurod, S. Norkobilov and I. Jamshid, “Features of Using the Energy-Saving LEACH Protocol to Control the Temperature of Stored Cotton Piles via a Wireless Network of Sensors”, International Journal of Discoveries and Innovations in Applied Sciences, Vol. 1, No. 5, pp. 278-283, 2021.
  • W. Sun and Y. Gao, “The Design of University Physical Education Management Framework based on Edge Computing and Data Analysis”, Wireless Communications and Mobile Computing, Vol. 122, pp. 1-16, 2021.
  • H. Khalid, S.J. Hashim and M.A. Chaudhary, “Cross-SN: A Lightweight Authentication Scheme for a Multi-Server Platform using IoT-Based Wireless Medical Sensor Network”, Electronics, Vol. 10, No. 7, pp. 790-810, 2021.
  • A.N. Kadhim and S.B. Sadkhan, “Security Threats in Wireless Network Communication-Status, Challenges, and Future Trends”, Proceedings of International Conference on Advanced Computer Applications, pp. 176-181, 2021.
  • S. Jain, S. Pruthi and K. Sharma, “Penetration Testing of Wireless Encryption Protocols”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 258-266, 2022.

Abstract Views: 155

PDF Views: 1




  • Elimination of Data Modification in Sensor Nodes of WSN Using Deep Learning Model

Abstract Views: 155  |  PDF Views: 1

Authors

B. Chellapraba
Department of Information Technology, Karpagam Institute of Technology, India
M.S. Kavitha
Department of Computer Science & Engineering, SNS College of Technology, India
K. Periyakaruppan
Department of Computer Science & Engineering, SNS College of Technology, India
D. Manohari
Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, India

Abstract


This study focuses on removing the possibility of malicious data manipulation in wireless sensor networks (WSN) by utilising a deep learning method. When training deep neural networks, datasets that have been the subject of an attack that alters the data are used as the building blocks. This is done in preparation for putting the networks to the test in the real world. We find out through simulation with a 70:30 cross-validation across a 10-fold sample size that the proposed technique is superior to the current state of the art in terms of the packet delivery rate, latency and throughput.

Keywords


Deep Learning Model, Data Modification, WSN, Throughput

References