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README.md

@@ -122,3 +122,20 @@ The dataset provides six predefined orderings in `orderings.json`. These orderin
 | `abrupt_drift` | `cicunsw_1` → `cicunsw_2` → `cicunsw_3` → `cicunsw_0` → `cicunsw_4` |
 
 These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.
+
+
+
+## Dataset Creation
+The details of dataset creation can be found in our paper: [link](https://arxiv.org/abs/2607.18289)
+
+
+## Citation
+When using the dataset, please cite:
+```
+@article{faber2026towards,
+  title={Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios},
+  author={Faber, Kamil and Smendowski, Mateusz and Corizzo, Roberto},
+  journal={arXiv preprint arXiv:2607.18289},
+  year={2026}
+}
+```

croissant.json

@@ -53,7 +53,7 @@
     "http://mlcommons.org/croissant/1.1",
     "http://mlcommons.org/croissant/RAI/1.0"
   ],
-  "citeAs": "https://www.unb.ca/cic/datasets/ids-2018.html; https://research.unsw.edu.au/projects/unsw-nb15-dataset",
+  "citeAs": "@misc{faber2026principled, title={Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios}, author={Faber, Kamil and Smendowski, Mateusz and Corizzo, Roberto}, year={2026}, eprint={2607.18289}, archivePrefix={arXiv}, primaryClass={cs.LG}, doi={10.48550/arXiv.2607.18289}, url={https://arxiv.org/abs/2607.18289}}",
   "creator": [
     {
       "@type": "sc:Person",