Awesome

MIDAS.Python

Python implementation of the MIDAS algorithm.

Demo

1. Open a terminal at the root MIDAS.Python/
2. set PYTHONPATH=src (Windows) or PYTHONPATH=src (Linux/macOS)
   • Because this is where the MIDAS package is
3. python example/Demo.py

It runs on data/darpa_processed.csv, using MIDAS-F with default parameters as in the C++ version, and will export anomaly scores to temp/Score.txt.

Requirement

• MIDAS Cores
  • numpy
  • numba: JIT compilation
• example/Demo.py
  • pyprojroot: Detect project root
  • sklearn: ROC-AUC
  • tqdm: Show progress

Note

Preliminary tests show the ROC-AUC difference of this version and the C++ version is less than 1e-7.

I tried to include some optimizations, but it is still order-of-magnitude times slower than the C++ version, e.g., 97ms vs. 5s for the NormalCore.
Therefore, this python implementation should only be used for understanding the high-level concepts of the algorithm.
For production environments or research projects, it is highly suggested to use our fully-optimized C++ version in the master branch.

Customization

Switch Cores

Uncomment the desired core at example/Demo.py:15-17 and comment out the rest.

Custom Dataset + Demo.py

You need to prepare two files:

• Data file
  • A header-less csv format file of shape [N,3]
  • Columns are sources, destinations, timestamps
  • Replace the path at example/Demo.py:9
  • E.g. data/DARPA/darpa_processed.csv
• Label file
  • A header-less csv format file of shape [N,1]
  • The corresponding label for data records
    • 0 means normal record
    • 1 means anomalous record
  • Replace the path at example/Demo.py:10
  • E.g. data/DARPA/darpa_ground_truth.csv

Custom Dataset + Custom Runner

1. Include MIDAS.NormalCore, MIDAS.RelationalCore or MIDAS.FilteringCore
2. Instantiate cores with required parameters
3. Call Call() on individual data records, it returns the anomaly score for the input record

Citation

If you use this code for your research, please consider citing our arXiv preprint

@misc{bhatia2020realtime,
    title={Real-Time Streaming Anomaly Detection in Dynamic Graphs},
    author={Siddharth Bhatia and Rui Liu and Bryan Hooi and Minji Yoon and Kijung Shin and Christos Faloutsos},
    year={2020},
    eprint={2009.08452},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

or our AAAI paper

@inproceedings{bhatia2020midas,
    title="MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams",
    author="Siddharth {Bhatia} and Bryan {Hooi} and Minji {Yoon} and Kijung {Shin} and Christos {Faloutsos}",
    booktitle="AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence",
    year="2020"
}