测量和分类IP使用场景:一种连续的神经树方法.
Zhenhui Li1, Fan Zhou1,2, Zhiyuan Wang1
1University of Electronic Science and Technology of China, Chengdu, 610054, China.
Scientific reports
|March 1, 2024
概括
本研究引入了一种新方法来分类IP地址使用场景,区分企业和家庭网络. 该模型有效地识别了使用模式,并在不同地区进行了泛化.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 通过IP地址了解用户行为对于欺诈预防和营销等应用至关重要.
- 现有的方法专注于IP地理位置和异常检测,忽视了IP使用场景分类.
- 一个IP地址的功能 (例如,私人企业与家庭宽带) 是一个尚未探索的领域.
研究的目的:
- 发起了第一次尝试对IP使用场景进行分类的尝试.
- 开发一种能够学习知识产权分配规则和复杂特征交互的模型.
- 评估模型的分类准确性和在不同地区的概括性.
主要方法:
- 从四个大规模地区收集了IP地址数据.
- 提出了一种基于神经树的新型连续神经树组合模型.
- 进行了广泛的实验来评估性能.
主要成果:
- 拟议的模型有效地揭示了更高阶的显著特征相互作用.
- 提高了IP使用场景的分类准确性.
- 证明了该模型从源到目标区域的概括能力.
结论:
- 开发的模型有效地对IP使用场景进行了分类.
- 这种方法提高了对IP地址功能的理解.
- 该模型显示强大的通用性,适用于各种网络环境.
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