基于大数据的网络信息传输的安全情况意识算法.
1Chongqing University of Arts and Sciences, Yongchuan, Chongqing, 402160, China.
Scientific reports
|November 7, 2025
概括
本研究介绍了一种新的三阶段算法,用于网络安全情况意识,通过机器学习增强网络流量的威胁识别. 新方法显著提高了检测恶意网络攻击的准确性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 大数据分析大数据分析
背景情况:
- 传统的安全机制正受到越来越复杂和多样化的恶意网络攻击的挑战.
- 在网络信息传输中有效识别威胁对于维护网络安全至关重要.
研究的目的:
- 提出一个新的三阶段算法,用于网络安全情况的认识.
- 通过机器学习和大数据处理,加强网络流量的威胁识别.
主要方法:
- 从网络流量流中创建特征向量 (统计,时间,基于流量的,关系特征).
- 混合特征选择使用分布式K-Means (D-KMeans) 进行集群和相互信息 (MI) 分析.
- 使用分布式K-最近邻近 (D-KNN) 模型进行网络流量分类.
主要成果:
- 该算法在CICIDS2017数据集上实现了98.91%的准确性,93.71%的精度,98.95%的回忆率和96.00%的F-Measure.
- 与最先进的方法相比,在统计学上显示了至少1.2%的精度显著增加.
- 验证了在大型网络环境中识别安全威胁的有效性和效率.
结论:
- 拟议的三阶段算法为网络流量分类和威胁识别提供了强大而可扩展的解决方案.
- 机器学习和大数据处理的整合显著提高了网络安全意识.
- 这种方法提供了一种有效的手段来打击复杂的恶意网络攻击.
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