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防火墙日志中异常检测方法的比较分析:整合安全日志的轻量级合成和人工生成的攻击检测
Adrian Komadina1, Ivan Kovačević1, Bruno Štengl1
1Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种用于在防火墙日志中生成现实的网络异常的新方法. 监督学习有效地检测到这些异常,与无监督方法不同,对现实世界的安全系统显示出希望.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 检测网络异常对于大型工业控制系统至关重要.
- 当前的方法通常依赖于有限的数据集和专门的机器学习 (ML).
- 需要在没有专用测试台的情况下实现现实的异常生成.
研究的目的:
- 提出一种用于在防火墙日志中生成现实的异常的新方法.
- 在工业控制网络中模拟真实攻击者的行动.
- 评估不同ML模型在检测这些产生的异常方面的有效性.
主要方法:
- 分析来自大型工业控制网络的防火墙日志.
- 开发一种方法来产生模拟攻击者行动的异常.
- 监督和无监督ML模型的比较,具有不同的特征工程和缩放技术.
主要成果:
- 无监督学习方法很难检测注入的异常.
- 生成的异常可以无地集成到现有的防火墙日志中.
- 监督学习方法表现出明显优于无监督学习方法的表现.
结论:
- 建议的异常生成方法是可行的,并产生现实的日志数据.
- 监督学习更适合用于检测工业控制网络防火墙日志中的异常.
- 这些发现表明,加强网络安全监控的实际方法.
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