Autoencoders for Anomaly Detection

This repository explores the cutting-edge field of anomaly detection using deep learning, particularly through the implementation of autoencoders. Paper regarding the code is in the following link: https://sbic.org.br/wp-content/uploads/2021/09/pdf/CBIC_2021_paper_37.pdf

Prerequisites

Before you begin, ensure you have met the following requirements:

You have installed Python 3.10, which is required to create the virtual environment.
You have a basic understanding of Python virtual environments.

Setting Up and Running the Project

To set up the Python environment and run the project, follow these steps:

1. Clone the Repository

First, clone the repository to your local machine using the following command:

git clone https://github.com/lucastakara/CBIC_21_Anomaly_Detection.git

2. Set Up the Python Virtual Environment

Navigate to the project directory and create a virtual environment using Python 3.10. The following steps are for Unix-based systems like Linux and macOS. If you are using Windows, the commands may differ slightly.

cd /path/to/your/repository  # Navigate to the cloned repository

python3.10 -m venv venv  # Create a virtual environment named 'venv'

Activate the virtual environment. The command to do this varies by operating system.

On macOS and Linux:

source venv/bin/activate

On Windows (use either Command Prompt or PowerShell):

.\venv\Scripts\activate

3. Install the Dependencies

With the virtual environment activated, install the project's dependencies:

pip install -r requirements.txt

4. Run the Project

Now that the environment is set up, you can run the project using:

python main.py

Next implementations

Implement K-fold Cross-Validation
Perform loss reconstruction with different loss functions (MSE, MAPE, RMSE)
Implement Hyperparameter tuning