C++ implementation of

• Real-time Streaming Anomaly Detection in Dynamic Graphs. Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos. (Under Review)
• MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos. AAAI 2020.

The old implementation is in another branch OldImplementation, it should be considered as being archived and will hardly receive feature updates.

• Features
• Demo
• Customization
• Online Articles
• MIDAS in other Languages
• Citation

• Finds Anomalies in Dynamic/Time-Evolving Graph: (Intrusion Detection, Fake Ratings, Financial Fraud)
• 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 55% more accurate and 929 times faster than the state of the art approaches
• Some experiments are performed on the following datasets:
  • DARPA
  • TwitterWorldCup2014
  • TwitterSecurity

If you use Windows:

1. Open a Visual Studio developer command prompt, we want their toolchain
2. cd to the project root MIDAS/
3. cmake -DCMAKE_BUILD_TYPE=Release -G "NMake Makefiles" -S . -B build/release
4. cmake --build build/release --target Demo
5. cd to MIDAS/build/release/src
6. .\Demo.exe

If you use Linux/macOS systems:

1. Open a terminal
2. cd to the project root MIDAS/
3. cmake -DCMAKE_BUILD_TYPE=Release -S . -B build/release
4. cmake --build build/release --target Demo
5. cd to MIDAS/build/release/src
6. ./Demo

The demo runs on MIDAS/data/DARPA/darpa_processed.csv, which has 4.5M records, with the filtering core.

The scores will be exported to MIDAS/temp/Score.txt, higher means more anomalous.

All file paths are absolute and "hardcoded" by CMake, but it's suggested NOT to run by double-click on the executable file.

Cores are instantiated at MIDAS/example/Demo.cpp:64-66, uncomment the chosen one.

You need to prepare three files:

• Meta file
  • Only includes an integer N, the number of records in the dataset
  • Use its path for pathMeta
• Data file
  • A header-less csv format file of shape [N,3]
  • Columns are sources, destinations, timestamps
  • Use its path for pathData
• 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
  • Use its path for pathGroundTruth

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

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
5. AIhub Interview

1. Golang by Steve Tan
2. Ruby by Andrew Kane
3. Rust by Scott Steele
4. R by Tobias Heidler
5. Python by Ritesh Kumar

If you use this code for your research, please consider citing our 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"
}