终端到终端异常子图检测通过子图级对比学习.
概括
本研究介绍了EndSubG,这是一个无监督的框架,通过联合建模子图分区和异常检测来检测异常子图 (AS). 它通过在没有事先监督的情况下识别复杂图形数据中的异常模式来提高安全性.
科学领域:
- 图形理论是指图形的理论.
- 机器学习是机器学习.
- 网络安全 网络安全
背景情况:
- 检测异常子图 (AS) 对安全至关重要,但由于巨大的子图空间和缺乏监督,这具有挑战性.
- 传统方法与未知的异常作斗争,深度学习模型往往忽略了子图中的协作节点行为.
- 现有的研究缺乏专门的评估指标来检测子图异常.
研究的目的:
- 提出一个端到端的无监督框架,EndSubG,用于联合子图分区和AS检测.
- 解决现有方法在处理复杂的子图空间和未知异常方面的局限性.
- 引入一个新的评估指标,AS-WNMI,用于子图异常检测.
主要方法:
- EndSubG通过预测边缘存在概率来模拟AS边界,为异常感知图嵌入和分区改进拓.
- 它形成了一个粗的子图网络,通过"子图-邻近"匹配模式来识别异常.
- 一个新的指标,AS-WNMI,旨在评估子图分区和异常识别.
主要成果:
- 与现有方法相比,EndSubG在合成和现实数据集上表现出卓越的性能.
- 该框架在曲线下的面积 (AUC),平均精度 (AP) 和新的AS-WNMI指标方面取得了很高的分数.
- 可视化提供了检测到的子图的直观分析.
结论:
- EndSubG为子图异常检测提供了一种有效的端到端无监督方法.
- 拟议的框架和评估指标推进了基于图表的异常检测领域.
- 这项工作提供了一个强大的解决方案,通过改进AS检测来提高高影响域的安全性.
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