README.Rmd

---
output: github_document
title: "MIDASwrappeR"
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

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R Wrapper around [C++](https://github.com/bhatiasiddharth/MIDAS) implementation  by [Siddharth Bhatia](https://github.com/bhatiasiddharth)

## Installation

You can install the released version of MIDASwrappeR from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("MIDASwrappeR")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("pteridin/MIDASwrappeR")
```

## Table of Contents
- [Features](#features)
- [Use Cases](#use-cases)
- [Installation](#getting-started)
- [Demo](#demo)
- [Datasets](#datasets)
- [MIDAS in other Languages](#midas-in-other-languages)
- [Online Articles](#online-articles)
- [Citation](#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](https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf). *Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos*. AAAI 2020.

## Use Cases
1. Intrusion Detection
2. Fake Ratings
3. Financial Fraud

## Example
```{r eval = FALSE}
library(MIDASwrappeR)
data("MIDASexample")
getMIDASScore(MIDASexample, undirected = T)
```
A vignette to explain how this package works is included. 

## Datasets
1. [DARPA](https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset): [Original Format](https://www.comp.nus.edu.sg/~sbhatia/assets/datasets/darpa_original.csv), [MIDAS format](https://www.comp.nus.edu.sg/~sbhatia/assets/datasets/darpa_midas.csv)
2. [TwitterWorldCup2014](http://odds.cs.stonybrook.edu/twitterworldcup2014-dataset)
3. [TwitterSecurity](http://odds.cs.stonybrook.edu/twittersecurity-dataset)

## MIDAS in other Languages
1. [C++](https://github.com/bhatiasiddharth/MIDAS) by [Siddharth Bhatia](https://github.com/bhatiasiddharth)
2. [Rust](https://github.com/scooter-dangle/midas_rs) and [Python](https://github.com/scooter-dangle/midas_rs/tree/master/python) by [Scott Steele](https://github.com/scooter-dangle)
3. [Ruby](https://github.com/ankane/midas) by [Andrew Kane](https://github.com/ankane)

## Online Articles
1. KDnuggets: [Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs](https://www.kdnuggets.com/2020/04/midas-new-baseline-anomaly-detection-graphs.html)
1. Towards Data Science: [Controlling Fake News using Graphs and Statistics](https://towardsdatascience.com/controlling-fake-news-using-graphs-and-statistics-31ed116a986f)
2. Towards Data Science: [Anomaly detection in dynamic graphs using MIDAS](https://towardsdatascience.com/anomaly-detection-in-dynamic-graphs-using-midas-e4f8d0b1db45)
3. Towards AI: [Anomaly Detection with MIDAS](https://medium.com/towards-artificial-intelligence/anomaly-detection-with-midas-2735a2e6dce8)

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

```{markup eval = FALSE}
@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}
}
```