The Online Social Network (OSN) has gained immense popularity, but with it comes security and privacy concerns, including the rise of fake and clone profiles. Fake profiles pose threats like phishing, stalking, and spamming, while clone profiles steal user identities and misuse them. This project aims to detect these issues on Twitter using a combination of rule-based fake profile detection and similarity measures for clone profile detection.
- Fake Profile Detection: Set of rules are employed to classify fake and genuine profiles.
- Clone Profile Detection: Utilizes Similarity Measures and Neural Networks (instead of C4.5 decision trees) to detect clone profiles.
- Enhanced Security: Aims to safeguard OSN users by identifying potential threats.
To use the project, follow these steps:
- Clone this repository to your local machine.
- Install the required libraries and dependencies (detailed in the installation section).
- Prepare your data for testing the detection methods.
- Run the appropriate detection scripts to identify fake and clone profiles.
- Make sure you have Python [version] installed.
- Clone this repository:
- Install the required packages using:
- Fake Profile Detection: A set of rules is implemented for classifying fake and genuine profiles.
- Clone Profile Detection:
- Similarity Measures: Compares attributes and network relationships of profiles for similarity.
- Neural Networks: Utilizes neural networks to detect clone profiles.
The project presents a comparison between the effectiveness of similarity measures and neural networks for clone profile detection, along with rule-based fake profile detection.
- Incorporate more advanced machine learning models.
- Extend the project to other social media platforms.
- Implement real-time detection and alerts for users.
Contributions are welcome! Please fork the repository and create a pull request with your improvements.
For questions or feedback, feel free to reach out to the project maintainer:
- Name: [Jallepalli Harsha Vardhan]
- Email: [jallepalli7981@gmail.com]