哈:一个有影响力的节点识别算法,基于枢纽触发的邻里分解和不对称的顺序反复模型
Min Zhao1, Junhan Ye1, Jiayun Li1
1Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
这项研究引入了一种新的算法,用于识别电网中的关键节点,提高对恶意攻击的安全性. 该方法提高了复杂网络结构中检测有影响力的节点的准确性.
科学领域:
- 电力系统工程 电力系统工程
- 网络科学 网络科学
- 网络安全 网络安全
背景情况:
- 电力网络面临来自恶意攻击的越来越多的安全威胁.
- 识别有影响力的节点对于电网安全和稳定至关重要.
- 复杂的网络,像电网一样,表现出高的集群系数和复杂的节点互连.
研究的目的:
- 提出一种用于评估电力网络中节点影响的新算法.
- 加强对易受威胁或能够传播威胁的关键节点的识别.
- 提高电网对网络攻击的安全性和弹性.
主要方法:
- 引入了网络定向策略和枢纽触发的社区分解.
- 开发了感染者和感染潜力的概念.
- 为影响计算构建了一个不对称的,按顺序重复的模型.
- 集成的多级邻近潜力用于枢纽节点影响评估.
主要成果:
- 拟议的算法与传统和最先进的方法相比,显示出更高的性能.
- 对六个电力网络的评估显示,敏感-感染-恢复 (SIR) 相关系数,不精确函数和算法分辨率有所改善.
- 该方法有效地区分了威胁传播过程中的功能节点差异.
结论:
- 新型节点影响评估算法为电力网络安全提供了更高的准确性和有效性.
- 该方法提供了一个强大的框架,用于识别复杂网络中的关键节点.
- 这项研究有助于加强电力基础设施的网络安全.
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