Intrusion Detection with Unsupervised Heterogeneous Ensembles using Cluster-based Normalization
Outlier detection has been shown to be a promising machine learning technique for a diverse array of fields and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but at a cost of poorer performance compared to their supervised counterparts. Recent work has explored ways of making up for this, such as creating ensembles of diverse models, or even diverse learning algorithms, to jointly classify data. While using unsupervised, heterogeneous ensembles of learning algorithms has been proposed as a viable next step for research, the implications of how these ensembles are built and used has not been explored.
Scott Ruoti, Scott Heidbrink, Mark O'Neill, Eric Gustafson, and Yung Ryn Choe. 2017. Intrusion detection with unsupervised heterogeneous ensembles using cluster-based normalization. In Proceedings of the 24th IEEE International Conference on Web Services. IEEE.