Malicious Data Injection Detection and Prediction in Wireless Sensor Network Using Improved Swarm Intelligence

Authors

  • Throvagunta Srinija B.TECH Student, Department of Computer Science and Engineering [CSO], Raghu Engineering College, Visakhapatnam, India Author
  • Potnuru Asrith B.TECH Student, Department of Computer Science and Engineering [CSO], Raghu Engineering College, Visakhapatnam, India Author
  • Dandu Mohan Pavan Satyanarayana Raju B.TECH Student, Department of Computer Science and Engineering [CSO], Raghu Engineering College, Visakhapatnam, India Author
  • Bora Balaji Basanth B.TECH Student, Department of Computer Science and Engineering [CSO], Raghu Engineering College, Visakhapatnam, India Author
  • Krishnardhula Pavan Kumar M.TECH [Ph. D] and Guide, Department of Computer Science and Engineering [CSO], Raghu Engineering College, Visakhapatnam, India Author

Keywords:

WSN, Malicious Data Injection Detection, Prediction, Improved Swarm Intelligence

Abstract

Due to their weakness, wireless sensor networks (WSNs) may be subject to detrimental effects both physically and remotely. Stated differently, a great deal of applications requiring wireless sensor networks require security. Sensor measurements are used to locate events such as floods and fires. Wireless sensor networks are vulnerable, so it's important to protect the network by detecting when fake data is entered. An algorithm to identify and eliminate malicious network traffic has been developed. The suggested improved swarm intelligence method is applied to multiple datasets in order to assess its performance. A simulator is used to test the algorithm. The study and simulation results show how to identify and remove malicious data from wireless sensor networks.

Downloads

Download data is not yet available.

References

Roy Sandip et al., "Secure data aggregation in wireless sensor networks: Filtering out the attacker's impact", IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 681-694, 2014.

I. S. Jacobs, C. P. Bean, G. T. Rado, H. Suhl, "Fine particles thin films and exchange anisotropy" in Magnetism, New York:Academic, vol. III, pp. 271-350, 1963.

K.Pradeepa, WR Anne, S.Duraisamy, "Design and implementation issues of clustering in Wireless Sensor Networks", International Journal of Computer Applications, vol. 47, no. 11, pp. 23, 2012.

T Kavitha, D.Sridharan, "Security vulnerabilities in Wireless Sensor Networks: A survey", Journal of Information Assurance and Security, vol. 5, pp. 31-44, 2010.

C.Alcaraz, J Lopez, R Roman, "Selecting Key Management Schemes for Wireless Sensor Networks application", Journal of Computers and Security (Elsevier), vol. 31, no. 8, pp. 956-966, 2012.

R Azarderskhsh, A Reyhani, "Secure clustering and symmetric key establishment in heterogeneous wireless sensor networks", Eurasip Journal on Wireless Communications and Networking Article ID: 893592, pp. 1-12, 2011.

AC Chan, C Castelluccia, "A security framework for privacy preserving data aggregation in wireless sensor networks", ACM Transactions on Sensor Networks (TOSN), vol. 7, no. 4, pp. 29, 2011.

S.Chatterjea, P. Havinga, "A Dynamic data aggregation scheme for Wireless Sensor Networks", Proc. ProRISC, pp. 56-60, 2003.

Dietrich, F. Dressler, "On the Lifetime of Wireless Sensor Networks", ACM Transactions on Sensor Networks, vol. 5, no. 1, pp. 1-38, 2009, [online] Available: 10.1145/1464420.1464425. [10] K. Kalpakis, K. Dasgupta, P. Namjoshi, "Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks", Computer Networks, vol. 42, no. 6, pp. 697- 716, August 2003.

Y. Xue, Y. Cui, K. Nahrstedt, "Maximizing lifetime for data aggregation in wireless sensor networks", ACM/Kluwer Mobile Networks and Applications (MONET) Special Issue on Energy Constraints and Lifetime Performance in Wireless Sensor Networks, pp. 853-64, Dec. 2005.

B. Hong, V.K. Prasanna, "Optimizing system lifetime for data gathering in networked sensor systems", Workshop on Algorithms for Wireless and Ad-hoc Networks (A-SWAN), August 2004.

K. Kalpakis, K. Dasgupta, P. Namjoshi, "Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks", Computer Networks, vol. 42, no. 6, pp. 697- 716, August 2003.

S. D. Roy, S. A. Singh, S. Choudhury and N. C. Debnath, "Countering sinkhole and black hole attacks on sensor networks using Dynamic Trust Management," 2021 IEEE Symposium on Computers and Communications, Marrakech, 2021, pp. 537-542.

C. Blum, and X. Li, “Swarm intelligence in optimization,” Swarm Intelligence . Springer, Berlin, Heidelberg. pp. 43-85, 2018.

D. Karaboga, “An idea based on honey bee swarm for numerical Optimization,” Erciyes university, engineering faculty, computer engineering department. 2015.

J. Singh, R. kumar and A. K. Mishra, "Clustering algorithms for wireless sensor networks: A review," 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2015, pp. 637- 642.

F. Ishmanov, and Y. Bin Zikria, “Trust Mechanisms to Secure Routing in Wireless Sensor Networks: Current State of the Research and Open Research Issues,” Journal of Sensors, 2020.

Benjie Chen, Kyle Jamieson, Hari Balakrishnan And Robert Morris “An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks,” in Proceedings of the wireless network, 2019.

Downloads

Published

16-04-2024

Issue

Section

Research Articles

How to Cite

Malicious Data Injection Detection and Prediction in Wireless Sensor Network Using Improved Swarm Intelligence. (2024). International Journal of Scientific Research in Science and Technology, 11(2), 608-619. https://ijsrst.com/index.php/home/article/view/IJSRST24112112

Similar Articles

1-10 of 148

You may also start an advanced similarity search for this article.