Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks
This research investigates the security challenges posed by worm propagation in wireless sensor networks (WSNs). A novel stochastic susceptible – infectious – vaccination – recovered model is introduced to analyse the dynamics of worm spread. Conditions for the existence of a unique global solution...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Mathematical and Computer Modelling of Dynamical Systems |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/13873954.2024.2396480 |
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| _version_ | 1850061183490457600 |
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| author | Hassan Tahir Anwarud Din Kamal Shah Bahaaeldin Abdalla Thabet Abdeljawad |
| author_facet | Hassan Tahir Anwarud Din Kamal Shah Bahaaeldin Abdalla Thabet Abdeljawad |
| author_sort | Hassan Tahir |
| collection | DOAJ |
| description | This research investigates the security challenges posed by worm propagation in wireless sensor networks (WSNs). A novel stochastic susceptible – infectious – vaccination – recovered model is introduced to analyse the dynamics of worm spread. Conditions for the existence of a unique global solution are examined, and necessary conditions for worm eradication are established. By incorporating random environmental fluctuations, the proposed model provides a more precise depiction of propagation dynamics than deterministic models. Empirical findings are presented to validate the model’s predictive accuracy across diverse scenarios, underscoring its robustness. Numerical simulations affirm the effectiveness of the analytical approach in understanding worm propagation within WSNs. The study offers valuable insights into worm dynamics and proposes a methodological framework to enhance network security. The findings underscore the significant role of stochastic systems in modelling and provide strategic perspectives for designing resilient defensive frameworks against worm attacks in WSNs. |
| format | Article |
| id | doaj-art-b49aa0ad39b041b3ab5dee76c5d8f3b8 |
| institution | DOAJ |
| issn | 1387-3954 1744-5051 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Mathematical and Computer Modelling of Dynamical Systems |
| spelling | doaj-art-b49aa0ad39b041b3ab5dee76c5d8f3b82025-08-20T02:50:19ZengTaylor & Francis GroupMathematical and Computer Modelling of Dynamical Systems1387-39541744-50512024-12-0130165868210.1080/13873954.2024.2396480Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networksHassan Tahir0Anwarud Din1Kamal Shah2Bahaaeldin Abdalla3Thabet Abdeljawad4School of Mathematical Sciences, Key Laboratory of MEA(Ministry of Education) & Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, ChinaDepartment of Mathematics, Sun Yat-sen University, Guangzhou, PR ChinaDepartment of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi ArabiaDepartment of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi ArabiaDepartment of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi ArabiaThis research investigates the security challenges posed by worm propagation in wireless sensor networks (WSNs). A novel stochastic susceptible – infectious – vaccination – recovered model is introduced to analyse the dynamics of worm spread. Conditions for the existence of a unique global solution are examined, and necessary conditions for worm eradication are established. By incorporating random environmental fluctuations, the proposed model provides a more precise depiction of propagation dynamics than deterministic models. Empirical findings are presented to validate the model’s predictive accuracy across diverse scenarios, underscoring its robustness. Numerical simulations affirm the effectiveness of the analytical approach in understanding worm propagation within WSNs. The study offers valuable insights into worm dynamics and proposes a methodological framework to enhance network security. The findings underscore the significant role of stochastic systems in modelling and provide strategic perspectives for designing resilient defensive frameworks against worm attacks in WSNs.https://www.tandfonline.com/doi/10.1080/13873954.2024.2396480Wireless sensor networks (WSNs)stochastic epidemic modelworm propagation |
| spellingShingle | Hassan Tahir Anwarud Din Kamal Shah Bahaaeldin Abdalla Thabet Abdeljawad Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks Mathematical and Computer Modelling of Dynamical Systems Wireless sensor networks (WSNs) stochastic epidemic model worm propagation |
| title | Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks |
| title_full | Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks |
| title_fullStr | Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks |
| title_full_unstemmed | Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks |
| title_short | Advances in stochastic epidemic modeling: tackling worm transmission in wireless sensor networks |
| title_sort | advances in stochastic epidemic modeling tackling worm transmission in wireless sensor networks |
| topic | Wireless sensor networks (WSNs) stochastic epidemic model worm propagation |
| url | https://www.tandfonline.com/doi/10.1080/13873954.2024.2396480 |
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