gen²Out: Detecting and Ranking Generalized Anomalies

---

Lee, MC., Shekhar, S., Faloutsos, C., Hutson, TN., and Iasemidis, L., gen2Out: Detecting and Ranking Generalized Anomalies. IEEE International Conference on Big Data (Big Data), 2021.

https://ieeexplore.ieee.org/abstract/document/9671550

Please cite the paper as:

@inproceedings{lee2021gen2out,
  title={{gen2Out:} Detecting and Ranking Generalized Anomalies},
  author={Lee, Meng-Chieh and Shekhar, Shubhranshu and Faloutsos, Christos and Hutson, T Noah and Iasemidis, Leon},
  booktitle={2021 IEEE International Conference on Big Data (Big Data)},
  year={2021},
  organization={IEEE},
}

Installation and Dependency

The experiment code is writen in Python 3 and built on a number of Python packages:

• matplotlib==3.5.0
• numpy==1.21.2
• scipy==1.7.3
• scikit_learn==1.0.2

Usage and Sample Output

Experiments of Fig. 6 in the paper could be reproduced by running the code directly. You could simply download/clone the entire repository and execute the code by

make demo

Acknowledgement

One part of our code is based on scikit-learn IsolationForest, downloaded from https://github.com/scikit-learn/scikit-learn/.

This implementation is according to the following paper:

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In 2008 8th IEEE International Conference on Data Mining (pp. 413-422). IEEE.