flow-midas: MIDAS wrapped by pybind11

CI	MSVC 2015	conda.recipe	pip builds	cibuildwheel
Status

This python package is a wrapper of this c++ source code. Thanks to the author!

Installation

pip install flow-midas

Example

import midas as md

# Load dataset
data = pd.read_csv("data/DARPA/darpa_processed.csv", names=['src', 'dst', 'timestamp'])
label = pd.read_csv("data/DARPA/darpa_ground_truth.csv", names=['label'])

models = {
    "NormalCore": md.NormalCore(2, 769, 42),
    "RelationalCore": md.RelationalCore(2, 769, 42, 0.5),
    "FilteringCore": md.FilteringCore(2, 769, 42, 0.5, 0.5),
}

for name, model in models.items():
    score = []
    for x in data.values:
        score.append(model(x[0], x[1], x[2]))
    auc = roc_auc_score(label["label"], score)
    print(f"{name} auc: {auc}")

    assert auc > 0

Development

Prerequisites

• A compiler with C++11 support
• Pip 10+ or CMake >= 3.4 (or 3.8+ on Windows, which was the first version to support VS 2015)
• Ninja or Pip 10+

Installation

Just clone this repository and pip install. Note the --recursive option which is needed for the pybind11 submodule:

git clone --recursive https://github.com/cliffxzx/flow-midas.git
pip install ./flow-midas

With the setup.py file included in this example, the pip install command will invoke CMake and build the pybind11 module as specified in CMakeLists.txt.

Building the documentation

Documentation for the example project is generated using Sphinx. Sphinx has the ability to automatically inspect the signatures and documentation strings in the extension module to generate beautiful documentation in a variety formats. The following command generates HTML-based reference documentation; for other formats please refer to the Sphinx manual:

• cd midas/docs
• make html

Citation

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

@misc{bhatia2020realtime,
    title={Real-Time Anomaly Detection in Edge Streams},
    author={Siddharth Bhatia and Rui Liu and Bryan Hooi and Minji Yoon and Kijung Shin and Christos Faloutsos},
    booktitle={Transactions on Knowledge Discovery from Data (TKDD)},
    year={2022}
}

or the 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 Conference on Artificial Intelligence (AAAI)",
    year="2020"
}