Detectify

Detectify is a Flask web application designed for comparative analysis of various anomaly detection algorithms. It empowers users to explore the performance of different algorithms on a variety of datasets.

Key Functionalities:

• Algorithm Selection: Choose one or more of our implemented Machine Learning algorithms for anomaly detection.
• Dataset Exploration: Select a univariate or multivariate dataset from our provided list.
• Model Training & Evaluation: The chosen algorithms are trained on the selected dataset in the backend.
• Performance Visualization: Results are presented through graphs and plots, including AUC curves.
• Evaluation Metrics: Performance is quantified using metrics like precision, recall, F1-score, and AUC score.
• Data Visualization (Univariate): Univariate datasets can be visualized to understand their data distribution.

Datasets

The anomaly detection algorithms in Detectify are evaluated on various datasets. You can find the datasets used in this project at the following repository: https://github.com/varad0207/Anomaly-Benchmarking-Datasets.git

Installation & Usage

Prerequisites: Python Version 3.9 to 3.11
Run the following commands in your terminal

1. Clone the repository

git clone https://github.com/varad0207/webApp-AD.git

2. Create a virtual environment in the root directory of the project

python -m venv venv

3. Activate the virtual environment
   Windows: venv/Scripts/activate MacOs/Linux: source venv/bin/activate

4. Install dependencies

pip install -r requirements.txt

5. Run the application

python app.py

This will start the Detectify web application, typically accessible at http://127.0.0.1:5000/ in your web browser.

Reference

This project is inspired by research presented in the paper: https://arxiv.org/pdf/2402.07281