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Deep Learning Based Hybrid Security Model in Wireless Sensor Network

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Abstract

Spatial positioning of the sensor nodes in the Wireless Sensor Networks (WSN) promotes remote monitoring of assets or target area in terms of regulating various environmental factors. One of the futuristic characteristic features of WSN is its ability act as autonomous, cooperative yet can be dynamic as well. Data collected from various sensor nodes need to be analysed and processed. Hence, distributed systems could be designed for collection process. Once when the data is collected, it might be aggregated based on the need and has to be sent to the base station. During the process of data transfer, it has to be encrypted. The most challenging task is that ensuring security for the huge data that is being generated with the help of the sensors. While ensuring security, it has to deal with the trade-offs with several other factors such as power consumption, delay, latency and data aggregation paves way for various researches. The idea of the work is to isolate the DoS attacker nodes by deploying a learning model. The proposed deep learning model comprises of few quality metrics of the network as initial nodes and progress the learning. The result shows significant changes while adopting deep learning model to routing, rerouting and data transmission across the network.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, M Kumarasamy College of Engineering, Karur, India

    N. M. Saravana Kumar

  2. Anna University, Chennai, India

    E. Suryaprabha

  3. Department of Information Technology, Vivekananda College of Engineering for Women, Namakkal, India

    K. Hariprasath

  4. School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia

    V. Vijayakumar

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Saravana Kumar, N.M., Suryaprabha, E., Hariprasath, K. et al. Deep Learning Based Hybrid Security Model in Wireless Sensor Network. Wireless Pers Commun 129, 1789–1805 (2023). https://doi.org/10.1007/s11277-023-10208-7

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