基于浏览模式的行为入侵者检测,自动分组请求的网页
Artur Wilczek1, Konrad Ciecierski1, Mariusz Kamola1
1R&D Division, NASK National Research Institute, 01-045 Warszawa, Poland.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
在线欺诈正在上升,但使用Web服务器日志的行为假冒检测可以识别攻击者. 我们的罗神经网络在检测欺诈会话时达到90%的准确性,为在线服务提供了可扩展的解决方案.
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:
- 在线欺诈和假冒攻击对数字服务构成重大威胁,特别是在银行业.
- 当前的身份验证方法往往缺乏强大的行为分析,严重依赖传统因素或有限的鼠标/键盘动态.
研究的目的:
- 调查在线银行业务中使用标准Web服务器日志用于行为假冒检测的有效性.
- 开发和评估一种可扩展的机器学习方法,用于识别欺诈性用户会话.
主要方法:
- 从Web服务器日志中提取和分析行为模式.
- 实现一个语神经网络,用于对用户网络会话进行分类.
- 开发一种使用低等级近似方法的自动化程序,用于分组网页以处理变化.
主要成果:
- 罗神经网络在对用户会话进行分类时实现了90%的准确性.
- 自动化页面分组程序提高了分类准确性,减少了手动数据分析.
- 这些方法在使用现实世界博客的银行场景中证明了其适用性.
结论:
- 来自Web服务器日志的行为模式可以有效地用于在线服务中的欺诈检测.
- 拟议的自动化方法为打击行为假冒提供了一个可扩展和可行的解决方案,提高了在线银行业务的安全性.
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