交互式学习用于网络异常监测和检测,在循环中使用人类指导
Dong Yang1, Ze Liu1, Songjie Wei1
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的方法,用于使用引导机器智能代理检测网络异常. 通过将人类专业知识与先进的算法集成,它显著提高了检测准确性和网络运营培训效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 传统的数据驱动解决方案与动态网络安全和管理员期望作斗争.
- 在高维,波动的时间序列网络数据中检测异常是一个重大挑战.
- 纯粹基于学习的方法对于有效的网络运行和维护是不够的.
研究的目的:
- 通过将机器智能与人类指导相结合,增强网络异常检测.
- 以马尔科夫决策流程 (MDP) 来建模异常检测,以提高性能.
- 为网络运营开发一个更适应和更准确的异常检测系统.
主要方法:
- 模拟异常检测作为马尔科夫决策过程 (MDP).
- 采用双深Q网络 (DDQN) 算法来训练一个异常检测代理.
- 使用多维时间卷积网络 (MTCN) 作为Q网络结构.
- 操作和维护人员对培训过程的综合交互指导.
主要成果:
- 与现有的基于学习的方法相比,在SMD数据集上实现了更高的精度和回忆率.
- 通过持续的人机交互来证明改进的模型融合.
- 展示了一个更快,更一致的模型培训程序.
结论:
- 拟议的人为导向的机器智能方法在网络异常检测方面更有效.
- 持续的人机交互优化模型性能和训练效率.
- 这种方法为管理复杂的网络安全挑战提供了强大的解决方案.
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