使用主动学习辅助的注意力对抗双自动编码器进行排名增强的异常检测.
Sidahmed Benabderrahmane1, James Cheney2, Talal Rahwan3
1Division of Science, NYUAD, New York University, Abu Dhabi, UAE. sidahmed.benabderrahmane@nyu.edu.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种使用自动编码器和主动学习来识别高级持久威胁 (APT) 的无监督异常检测方法. 这种方法显著提高了检测准确度,使用最小的标记数据,减少了手动标记的努力.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 异常检测检测异常检测
背景情况:
- 先进的持久性威胁 (APT) 由于其隐蔽性质,存在持续的网络安全挑战.
- 监督学习用于APT检测需要大量的标记数据,这在现实世界中往往是不可用的.
- 现有的方法与网络安全中常见的高度不平衡的数据集作斗争.
研究的目的:
- 开发一个有效的无监督异常检测框架来识别APT.
- 利用主动学习来最大限度地减少对广泛的手动数据标签的需求.
- 在复杂环境中提高APT检测的准确性和效率.
主要方法:
- 使用自动编码器进行无监督异常检测.
- 整合了一个活跃的学习循环,通过查询不确定样本来代地完善检测模型.
- 提出了注意力对抗双自动编码器框架用于异常检测.
- 评估了不平衡的真实世界来源跟踪数据集的框架.
主要成果:
- 通过积极学习实现了检测率的显著改善.
- 与现有的异常检测方法相比,证明了卓越的性能.
- 在高度不平衡的数据集中成功检测到类似APT的攻击 (数据只有0.004%).
- 在多个操作系统 (Android,Linux,BSD,Windows) 中验证了框架.
结论:
- 拟议的自动编码器和主动学习方法有效地检测到APT异常,降低了标签成本.
- 该框架为在数据稀缺和不平衡的环境中检测网络安全异常提供了有希望的解决方案.
- 积极学习反复增强模型性能,使其适应不断变化的威胁.
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