Awesome

MIDAS implementation in Python

Python implementation of C++ code by Siddharth Bhatia

Installation

You can install and use the package by cloning this repository in your project folder:

git clone https://github.com/ritesh99rakesh/pyMIDAS.git

Table of Contents

• Features
• Use Cases
• Installation
• Demo
• Datasets
• MIDAS in other Languages
• Online Articles
• Citation

Features

• Finds Anomalies in Dynamic/Time-Evolving Graphs
• Detects Microcluster Anomalies (suddenly arriving groups of suspiciously similar edges e.g. DoS attack)
• Theoretical Guarantees on False Positive Probability
• Constant Memory (independent of graph size)
• Constant Update Time (real-time anomaly detection to minimize harm)
• Up to 48% more accurate and 644 times faster than the state of the art approaches

For more details, please read the paper - MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos. AAAI 2020.

Use Cases

1. Intrusion Detection
2. Fake Ratings
3. Financial Fraud

Example

Inside the pyMIDAS directory

from midas import midas
import pandas as pd

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

# Anomaly Scores
anomaly_scores = midas(
    data,
    num_rows=2,
    num_buckets=769
)

For more examples, refer to examples folder of this repository.

Datasets

1. DARPA: Original Format, MIDAS format
2. TwitterWorldCup2014
3. TwitterSecurity

MIDAS in other Languages

1. C++ by Siddharth Bhatia
2. Rust by Scott Steele
3. Ruby by Andrew Kane

Online Articles

1. KDnuggets: Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs
2. Towards Data Science: Controlling Fake News using Graphs and Statistics
3. Towards Data Science: Anomaly detection in dynamic graphs using MIDAS
4. Towards AI: Anomaly Detection with MIDAS

Citation

If you use this code for your research, please consider citing our paper.

@article{bhatia2019midas,
  title={MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams},
  author={Bhatia, Siddharth and Hooi, Bryan and Yoon, Minji and Shin, Kijung and Faloutsos, Christos},
  journal={arXiv preprint arXiv:1911.04464},
  year={2019}
}