Welcome to the world of spam detection, where we strive to craft a seamless environment free from unwanted digital intrusions. The Nash-Detect code, devised for KDD 2020 by esteemed researchers, utilizes Nash Reinforcement Learning to train a robust spam review detector. In this guide, we’ll walk you through the steps to set up and run the code, as well as provide some troubleshooting tips along the way.

Overview of Nash-Detect

Before we dive into the setup, let’s understand what Nash-Detect does. Imagine a game of chess, where one player (the spammer) aims to trick their opponent (the defender) into exposing weaknesses. Nash-Detect embodies this concept, employing five distinct strategies for spamming that blend into a mixed strategy, just like a chess player using various openings. The algorithm comprises various base detectors that contend with these strategies through a minimax game approach making it robust against spam attacks.

Setup Instructions

To get started with Nash-Detect, follow these setup instructions:

1. Download the Yelp Spam Review Datasets. You’ll need to request access by emailing ytongdou@gmail.com.
2. Unzip the dataset file in the root directory of the project.
3. Clone the project and install the required packages using the following commands:

git clone https://github.com/YingtongDou/Nash-Detect.git
cd Nash-Detect
pip3 install -r requirements.txt

Ensure that you have Python 3.6 or a later version installed.

Running Nash-Detect

Once your setup is complete, here’s how to run the code effectively:

1. Execute attack_generation.py with mode = Training to produce fake reviews for training.
2. Run worst_case.py to analyze the worst-case performance of single attacks against single detectors.
3. Initiate training by running training.py to cultivate a robust detector using Nash-Detect.
4. Run attack_generation.py with mode = Testing to create fake reviews for testing.
5. Finally, execute testing.py to assess the performance of the optimal detector trained by Nash-Detect and additional baseline detectors.

Note that all generated fake reviews are stored in the Training and Testing directories, allowing you to forgo Steps 1 and 4 to directly engage in game and evaluation code.

Repository Structure

The organization of the repository is as follows:

• Attack: Contains four spamming attack strategies, along with the Singleton attack implemented in attack_generation.py.
• Detector: Contains the implementations and evaluations of five spam detectors.
• Testing: Houses the generated fake reviews for testing.
• Training: Stores the generated fake reviews for training.
• Utils: Includes helper functions for loading datasets, training, and testing along with feature extraction utilities.

Troubleshooting

While setting up Nash-Detect, you might run into some issues. Here are a few troubleshooting tips:

• Python Version: Ensure you are using the correct Python version (3.6 or later). You can check your Python version by running python --version in your terminal.
• Dependencies: If you encounter any import errors, double-check your installation of required packages listed in requirements.txt.
• Dataset Issues: Verify that you have correctly unzipped the Yelp dataset in the root directory. The project won’t work without the correct dataset structure.

If you’re still having trouble, feel free to reach out for assistance or insights. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.