在无线传感器网络中传播恶意代码的非线性随机模型的新型数值解决方案,使用高阶光谱拼接技术
Junjie Zhu1,2, Misbah Ullah3, Saif Ullah4
1School of Mathematics, Shandong University, Jinan, 250100, China.
Scientific reports
|January 2, 2025
概括
本研究引入了一个随机模型,以更好地理解在无线传感器网络 (WSN) 中传播的恶意代码. 新模型捕捉了网络的不确定性,以改善安全分析和控制策略.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 数学建模的数学建模
背景情况:
- 无线传感器网络 (WSN) 由于其开放性,容易受到恶意代码的传播.
- 现有的决定性模型不能完全捕捉WSN中固有的随机性和不确定性.
- 随机方法对于全面了解WSN中的恶意代码动态至关重要.
研究的目的:
- 为分析WSN中的恶意代码分布动态制定一个一般的随机分隔模型.
- 将随机性纳入经典的确定性模型,以更准确地表示代码传播.
- 分析随机模型的稳定性,并提出控制策略.
主要方法:
- 制定一个一般的随机区块模型.
- 将随机性纳入确定性模型,以考虑不可预测性.
- 模型稳定性的理论分析,包括随机性.
- 对数值解决方案应用一个更高阶的光谱调配技术.
- 进行全面的模拟来验证结果并分析参数的影响.
主要成果:
- 开发了一个随机模型来表示WSN中的恶意代码传播.
- 理论分析为随机模型提供了稳定性结果.
- 频谱合技术证明了准确性和数值稳定性.
- 模拟验证了模型的有效性,并说明了参数的影响.
结论:
- 与确定性模型相比,随机模型在WSN中提供了更合适的恶意代码动态表示.
- 频谱数值方案有效地捕捉了WSN固有的不确定性和复杂性.
- 该研究提供了对控制这些网络中传播的恶意代码的见解.
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