KerolosAtef / Generating-fake-tasks-using-CGAN-and-CGAN-WGAN

Generate fake tasks using CGAN and WGAN with Wassertein loss function

This project uses and explain how to use GANs with tabular data not only with images

Notes

This repo contains 2 notebooks which are the same but the only difference that one uses binary cross entropy as the loss function of the discriminator and the other uses wistern loss function

Overview

Fake task attack is critical for Mobile Crowdsensing system (MCS) that aim to clog the sensing servers in the MCS platform and drain more energy from participants’ smart devices. Typically, fake tasks are created by empirical model such as CrowdSenSim tool. Recently, cyber criminals deploy more intelligent mechanisms to create attacks. Generative Adversarial Network (GAN) is one of the most powerful techniques to generate synthetic samples. GAN considers the entire data in the training dataset to create similar samples. This project aims to use GAN to create fake tasks and verify fake task detection performance.

Important dependencies

pip install numpy 
pip install tensorflow-gpu==2.9.1
pip install pandas 
pip install seaborn 
pip install matplotlib 
pip install sklearn
pip install imbalanced-learn

Project Main steps

1. Download MCS dataset which and Split the dataset into training dataset (80%) and test dataset (20%)
2. Implement classic classifiers (Adaboost and RF) and train them
3. Verify detection performance using test dataset and present results comparison in bar chart
4. Implement a CGAN model and train it.
5. Generate synthetic fake tasks via Generator network in CGAN after the training procedure
6. Mix the generated fake tasks with the original test dataset to obtain a new test dataset
7. Obtain Adaboost and RF detection performance using the new test dataset and present results in bar chart
8. Consider the Discriminator to as the first level classifier and RF/Adaboost as the second level classifier

Results

The generated tasks from the generator are robust and succussed to fault the classic ML algorithms because it is tried to generate tasks very close to the real one, so the models can’t determine it and the accuracies has been decreased from 0.92 to 0.575 in the Adaboost model and has been decreased from 0.993 to 0.590 in the Random Forest model.
In the cascade approach the discriminator helped the models because it can filter the fake tasks, so after the filtering it out the accuracies increased again to 0.926 in Adaboost and to 0.993 in the Random Forest model and this result is approximately one before mixing which means that the discriminator filtered