对恶意软件传播应用微积分计算:一种基于分数的方法来进行威胁分析
Nausheen Razi1, Muhammad Bilal Riaz2,3, Ambreen Bano4
1Department of Mathematics, Quaid-i-Azam University, Islamabad, Pakistan.
PloS one
|January 8, 2025
概括
这项研究引入了一个分数分数 (FF) 衍生模型来分析恶意软件的传播,揭示了对理解网络威胁和开发更好的防病毒策略至关重要的记忆效应.
科学领域:
- 计算机科学 计算机科学
- 数学建模的数学建模
- 网络安全 网络安全
背景情况:
- 恶意软件和网络犯罪是计算机网络中普遍存在的威胁.
- 现有的防病毒解决方案昂贵,并且根据恶意软件类型而异.
- 数学模型对于理解恶意软件的行为至关重要.
研究的目的:
- 为了研究记忆效应对恶意软件传播的影响,使用碎形分数 (FF) 导数.
- 分析恶意软件动态的数学FF模型的存在,独特性和稳定性.
- 将FF模型与恶意软件传播的经典模型进行比较.
主要方法:
- 使用固定点理论进行理论分析.
- 基于拉格朗日插值的数值算法的开发.
- 使用Matlab R2016a.进行模拟.
- 对各种模型参数的灵敏度分析.
主要成果:
- FF模型有效地捕捉了恶意软件传播中的内存效应.
- 不同的FF命令显著影响受感染节点的动态.
- 敏感性分析突出了诸如感染率和免疫力等参数的影响.
结论:
- FF衍生提供了恶意软件动态中内存效应的详细描述.
- 反病毒软件开发可以从结合FF命令和参数中获益.
- 了解通过FF衍生品传播的恶意软件有助于防止网络犯罪灾难.
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