H. Ma, B. Ghojogh, M. N. Samad, D. Zheng and M. Crowley, "Isolation Mondrian Forest for Batch and Online Anomaly Detection," 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 2020, pp. 3051-3058, doi: 10.1109/SMC42975.2020.9283073.

The algorithm fuses two ideas, "isolation" from ensemble trees methods for anomaly detection and "Mondrian forests" which can learn flexible regression models from streaming data.

Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2020). Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs. ArXiv:2005.07427 [Cs, Stat]. http://arxiv.org/abs/2005.07427