3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model

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Published: Sep 20, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i8.7926
Keywords:
Binomial Distribution based Dwarf Mongoose Optimization (BD-DMO) algorithm, Identity Matrix Function-Kalman Filter (IMF-KF), Cosine Similarity distance integrated Sammon Mapping learning layer- Deep Neural Network (CSDSM-DNN), Wireless Sensor Networks(WSN), Intrusion Detection System(IDS)

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K. Santhi
Department (Computer Science and Engineering), Panimalar Engineering College, Poonamalle, Chennai, Tamilnadu, India
B. Sowmiya
Department ( Computing Technologies), SRM Institute of Science and Technologies, Tech park, SRMIST, Kattankulathur, Chennai, India
R. Shalinirajan
Department (School of Computing), Sathyabama Institute of science and Technology, Jeppiaar nagar, Rajiv Gandhi salai,Chennai, Tamilnadu, India
V. Vijayashanthi
Department (Artificial intelligence and Data Science), Vel tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College, Avadi, Tamilnadu, India
V. Vasudhevan
Department (Electronics and Communication Enginnering), Panimalar Engineering College, Poonamalle, Chennai, Tamilnadu, India

Abstract

In Wireless Sensor Networks (WSNs), intrusion aims indegrading or even eliminating the capacity of these networks for providing their functions. Thus, in recent years, several ideas are brought and employed. However, these techniques still did not fulfill their requirements in attaining better classification accuracy. This paper proposes a novel Cosine Similarity Distance integrated Sammon Mapping learning layer-Deep Neural Network (CSDSM-DNN)-centricIntrusion Detection Model (IDM) in WSNfor attaining better outcomes. Initially, the nodes are clustered; after that, utilizing Binomial Distribution based Dwarf Mongoose Optimization (BD-DMO), the cluster heads are selected. Then, theIdentity Matrix Function-Kalman Filter (IMF-KF) identified the optimal route. Subsequently, the data is transferred via the secured route. The transferred data is pre-processed and then, the important features are selected. Lastly, to classify whether the data is attacked or non-attacked, the selected features are given into the CSDSM-DNN. Therefore, with the prevailing approaches, the experiential outcomes are evaluated and analogized and it exhibits the proposed model’s higher reliability and efficacy.

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How to Cite
Santhi, K., Sowmiya, B., Shalinirajan, R., Vijayashanthi, V., & Vasudhevan, V. (2023). 3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8), 81–89. https://doi.org/10.17762/ijritcc.v11i8.7926
Issue
Vol. 11 No. 8 (2023)
Section
Articles

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