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Improving Intrusion Detection Systems using Zero-Shot Recognition via Graph Embeddings

机译：通过图嵌入使用零射击识别来改进入侵检测系统

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摘要

In order to detect insider threats, anomaly-based intrusion detection must learn profiles of normal user behavior. However this is particularly difficult when historical audit data is scarce. Zero-shot learning can address this limitation by compensating the absence of examples with semantic knowledge, allowing to better estimate behavior of unknown users. In this paper, we address insider threat detection in two use cases where historical user data is unavailable or obsolete. We extend an existing intrusion detection system by adding information describing user positions, roles and projects assignments. These semantic descriptions are encoded via graph embeddings. Experimental results show that providing this additional context improves insider threat detection significantly. This suggests that zero-shot learning is a promising way of improving intrusion detection systems.
机译：为了检测内部威胁，基于异常的入侵检测必须学习正常用户行为的配置文件。但是，在缺乏历史审计数据时，这尤其困难。零镜头学习可以通过用语义知识补偿示例的缺失来解决此限制，从而可以更好地估计未知用户的行为。在本文中，我们在历史用户数据不可用或已过时的两个用例中讨论了内部威胁检测。通过添加描述用户位置，角色和项目分配的信息，我们扩展了现有的入侵检测系统。这些语义描述是通过图形嵌入进行编码的。实验结果表明，提供此附加上下文可以显着改善内部威胁检测。这表明零镜头学习是改进入侵检测系统的一种有前途的方法。

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