1. Beggel, L., Kausler, B.X., Schiegg, M., Pfeiffer, M., Bischl, B.: Time series anomaly detection based on Shapelet learning. Comput. Stat. 34(3), 945–976 (2019). https://doi.org/10.1007/s00180-018-0824-9. https://ideas.repec.org/a/spr/compst/v34y2019i3d10.1007_s00180-018-0824-9.html
2. Callegari, C., Gazzarrini, L., Giordano, S., Pagano, M., Pepe, T.: A novel PCA-based network anomaly detection. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–5, July 2011. https://doi.org/10.1109/icc.2011.5962595
3. CyberZHG: Keras self-attention (2018). https://github.com/CyberZHG. gitHub repository
4. Davis, N., Raina, G., Jagannathan, K.P.: LSTM-based anomaly detection: detection rules from extreme value theory. CoRR abs/1909.06041 (2019). http://arxiv.org/abs/1909.06041
5. Eiteneuer, B., Niggemann, O.: LSTM for model-based anomaly detection in cyber-physical systems (2020)