[17] J. H. Friedman and B. E. Popescu, “Predictive learning via rule ensembles,” The Annals of Applied Statistics, vol. 2, no. 3, 2008.
[18] G. Vilone, L. Rizzo, and L. Longo, “A comparative analysis of rule-based, model-agnostic methods for explainable artificial intelligence,” in *Irish Conference on Artificial Intelligence and Cognitive Science*, 2020.
[19] M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” in ACM SIGSAC Conference on Computer and Communications Security (CCS), 2017.
[20] R. Li, Q. Li, J. Zhou, and Y. Jiang, "Adriot: An edge-assisted anomaly detection framework against iot-based network attacks," IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10576-10587, 2022.
[21] R. Li, Q. Li, Y. Huang, W. Zhang, P. Zhu, and Y. Jiang, "Iotenssemble: Detection of botnet attacks on internet of things," in European Symposium on Research in Computer Security (ESORICS), 2022.
[22] B. Scholkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Comput., vol. 13, no. 7, pp. 1443-1471, 2001.
[23] L. Ruff, N. Gornitz, L. Deecke, S. A. Siddiqui, R. A. Vandermeulen, A. Binder, E. Müller, and M. Kloft, “Deep one-class classification,” in International Conference on Machine Learning (ICML), 2018.
[24] S. Itani, F. Lecron, and P. Fortemps, “A one-class classification decision tree based on kernel density estimation,” Appl. Soft Comput., vol. 91, p. 106250, 2020.
[25] F. T. Liu, K. M. Ting, and Z. Zhou, “Isolation forest,” in IEEE International Conference on Data Mining (ICDM), 2008.
[26] H. Xu, G. Pang, Y. Wang, and Y. Wang, "Deep isolation forest for anomaly detection," CoRR, vol. abs/2206.06602, 2022.
[27] D. P. Kingma and M. Welling, "Auto-encoding variational bayes," in International Conference on Learning Representations (ICLR), 2014.
[28] J. Kauffmann, K.-R. Müller, and G. Montavon, "Towards explaining anomalies: A deep taylor decomposition of one-class models," Pattern Recognition, vol. 101, p. 107198, 2020.
[29] D. Kazhdan, B. Dimanov, M. Jamnik, and P. Liò, “MEME: generating RNN model explanations via model extraction,” CoRR, vol. abs/2012.06954, 2020.
[30] P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, M. Kloft, and K. Müller, "Explainable deep one-class classification," in International Conference on Learning Representations (ICLR), 2021.
[31] Z. Kong and K. Chaudhuri, "Understanding instance-based interpretability of variational autoencoders," in Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.
[32] J. Crabbé and M. van der Schaar, “Label-free explainability for unsupervised models,” in International Conference on Machine Learning (ICML), 2022.
[33] O. Eberle, J. Böttner, F. Kräutli, K. Müller, M. Valleriani, and G. Montavon, “Building and interpreting deep similarity models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 3, pp. 1149–1161, 2022.
[34] J. Sipple, "Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure," in International Conference on Machine Learning (ICML), 2020.