goMidas

Go 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/goMidas.git

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

Intrusion Detection
Fake Ratings
Financial Fraud

Usage

There are two ways to use goMidas:

Use the Midas and MidasR functions in the package in this repository:

Example:

package main

import (
    "fmt"
    goMidas "github.com/ritesh99rakesh/goMidas"
)

func main() {
    src := []int{2, 2, 3, 3, 5, 5, 7, 11, 1, 2}
    dst := []int{3, 3, 4, 4, 9, 9, 73, 74, 75, 76}
    times := []int{1, 1, 2, 2 ,2, 2, 2, 2, 2, 2}
    numRows := 4
    numBuckets := 769

    // get anomaly scores
    anomalyScore := goMidas.Midas(src, dst, times, numRows, numBuckets)
    fmt.Println(anomalyScore)
}

Use the main.go provided in example folder in this repository to run MIDAS algorithm from command line.

The file main.go just requires you to specify the data.csv file containing containing src, dst and timestamps.

You just have to run:

go run main.go -input <input-file> -<other-optional-arguments>

Complete details for the available arguments:

-alpha float
    Alpha: Temporal Decay Factor. Default is 0.6 (default 0.6)
-buckets int
    Number of buckets. Default is 769 (default 769)
-input string
    Input File. (Required)
-norelations
    To run Midas instead of Midas-R.
-output string
    Output File. Default is scores.txt (default "scores.txt")
-rows int
    Number of rows/hash functions. Default is 2 (default 2)
-undirected
    If graph is undirected.

For more details and to know how to find AUC of the anomaly scores, refer example folder in this repository.

Datasets

MIDAS in other Languages

Online Articles

KDnuggets: Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs
Towards Data Science: Controlling Fake News using Graphs and Statistics
Towards Data Science: Anomaly detection in dynamic graphs using MIDAS
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}
}

Issues

If you find any problem with the code, please raise an issue.

ritesh99rakesh/goMidas