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Learning the Propagation of Worms in Wireless Sensor Networks

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Wireless Internet (WiCON 2022)

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Abstract

Wireless sensor networks (WSNs) are composed of spatially distributed sensors and are considered vulnerable to attacks by worms and their variants. Due to the distinct strategies of worms propagation, the dynamic behavior varies depending on the different features of the sensors. Modeling the spread of worms can help us understand the worm attack behaviors and analyze the propagation procedure. In this paper, we design a communication model under various worms. We aim to learn our proposed model to analytically derive the dynamics of competitive worms propagation. We develop a new searching space combined with complex neural network models. Furthermore, the experiment results verified our analysis and demonstrated the performance of our proposed learning algorithms.

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Notes

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Authors and Affiliations

  1. University of Delaware, Newark, DE, 19711, USA

    Yifan Wang, Siqi Wang & Guangmo Tong

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  1. Yifan Wang
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  2. Siqi Wang
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  3. Guangmo Tong
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Correspondence to Yifan Wang .

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Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Zygmunt J. Haas

  2. The University of Texas at Dallas, Richardson, TX, USA

    Ravi Prakash

  3. Texas A&M University-Kingsville, Kingsville, TX, USA

    Habib Ammari

  4. The University of Texas at Dallas, Richardson, TX, USA

    Weili Wu

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Wang, Y., Wang, S., Tong, G. (2023). Learning the Propagation of Worms in Wireless Sensor Networks. In: Haas, Z.J., Prakash, R., Ammari, H., Wu, W. (eds) Wireless Internet. WiCON 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-27041-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-27041-3_8

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