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一种使用高阶谱配置技术求解无线传感器网络中恶意代码传播的非线性随机模型的新数值方法。

A novel numerical solution of nonlinear stochastic model for the propagation of malicious codes in Wireless Sensor Networks using a high order spectral collocation technique.

作者信息

Zhu Junjie, Ullah Misbah, Ullah Saif, Riaz Muhammad Bilal, Saqib Abdul Baseer, Alamri Atif M, AlQahtani Salman A

机构信息

School of Mathematics, Shandong University, Jinan, 250100, China.

School of Mathematics and Data Sciences, Changji University, Changji, 831100, China.

出版信息

Sci Rep. 2025 Jan 2;15(1):228. doi: 10.1038/s41598-024-82033-2.

DOI:10.1038/s41598-024-82033-2
PMID:39747261
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11697034/
Abstract

The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model's dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks' inherent uncertainties and complex nature.

摘要

无线传感器网络(WSNs)的开放性使其容易成为恶意代码传播的目标,对其安全性构成重大且持续的威胁。近年来的文献中研究了各种数学模型,以了解无线传感器网络中恶意代码传播的动态过程和控制方法。然而,由于无线传感器网络中存在固有的随机性和不确定性,随机建模方法对于全面理解恶意代码在无线传感器网络中的传播至关重要。在本文中,我们建立了一个通用的随机 compartmental 模型,用于分析无线传感器网络中恶意代码分布的动态过程,并提出可能的控制方法。我们将随机性纳入经典的确定性模型中,以反映代码传播中固有的不可预测性,从而更恰当地表示动态过程。我们进行了包括具有随机性的模型稳定性结果在内的基本理论分析。此外,我们将高阶谱配置技术应用于所提出的随机模型的数值求解。给出了模型的准确性和数值稳定性。最后,进行了全面的仿真,以验证理论结果并描述参数对模型动态行为的影响。本研究通过谱数值方案的实现,将随机性纳入无线传感器网络中恶意代码传播的确定性模型,有助于捕捉这些网络固有的不确定性和复杂特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/b111e2d78f72/41598_2024_82033_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/a4c8b0cd4c68/41598_2024_82033_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/ebcfb69ba33a/41598_2024_82033_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/e3a8713e1140/41598_2024_82033_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/8f39a81074d6/41598_2024_82033_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/d981a851ce79/41598_2024_82033_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/7eba300a3445/41598_2024_82033_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/ba9f81433566/41598_2024_82033_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/edfee0bc60c0/41598_2024_82033_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/b111e2d78f72/41598_2024_82033_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/a4c8b0cd4c68/41598_2024_82033_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/ebcfb69ba33a/41598_2024_82033_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/e3a8713e1140/41598_2024_82033_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/8f39a81074d6/41598_2024_82033_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/d981a851ce79/41598_2024_82033_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/7eba300a3445/41598_2024_82033_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/ba9f81433566/41598_2024_82033_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/edfee0bc60c0/41598_2024_82033_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9614/11697034/b111e2d78f72/41598_2024_82033_Fig8_HTML.jpg

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