基于特征选择和优化烟花算法的网络安全分析.
Liang Zhou1, Chang Liu1, Li Tian1
1State Grid Hubei Electric Power Research Institute, Hubei, 430077, Wuhan, China.
Scientific reports
|December 19, 2025
概括
本研究介绍了一种改进的烟花算法,用于网络安全,增强数据处理和威胁检测灵敏度. 虽然高效,但它显示了复杂数据集的潜在过度适应,需要进一步的概括改进.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的网络安全方法与高维动态数据作斗争,导致功能选择差,威胁检测灵敏度低.
- 不断发展的网络威胁需要先进的分析模型来实现实时网络安全.
- 现有的算法在处理大规模的动态网络数据时缺乏适应性和效率.
研究的目的:
- 为增强网络安全分析提出一个多目标,多标签的功能选择模型.
- 将优化的烟花算法与模糊神经网络集成在一起,以改进实时威胁检测.
- 解决传统网络安全方法在数据处理能力,敏感性和效率方面的局限性.
主要方法:
- 开发了一种改进的烟花算法模型,包含高斯运算符和自适应函数.
- 集成的模糊神经网络用于增强实时威胁响应能力.
- 在不同规模的Palmer Penguin,时尚MNIST和自行车共享数据集上验证了模型.
主要成果:
- 实现了5000个样本的数据处理能力,比基线算法提高了66%.
- 经过证明的检测灵敏度在70%至100%之间,比传统方法高出30%点.
- 减少了50%的适应性调整时间,表明显著的效率提升.
- 在中型数据集上观察到潜在的过拟合或不充分的概括,在综合性表现中得分为5/10.
结论:
- 拟议的模型为动态网络安全分析提供了一个强大的框架,大大提高了处理能力和检测灵敏度.
- 优化的烟花算法显示了网络安全应用程序的提高效率.
- 复杂数据环境中的可扩展性约束和概括性需要进一步调查和模型改进.
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