The Bot Detection System is a robust solution designed to identify and classify social media bots in real-time. It leverages advanced machine learning models, real-time data streaming, and secure APIs to provide accurate bot detection with strong data privacy measures.
- Real-Time Data Ingestion: Uses Apache Kafka for efficient data streaming.
- Advanced Machine Learning: Random Forest Classifier with TF-IDF features and sentiment analysis.
- RESTful API: Built with FastAPI for quick and reliable predictions.
- Data Security: Implements encryption and anonymization for privacy protection.
- Comprehensive Reporting: Generates CSV reports for easy data analysis.
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Languages: Python
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Libraries: Pandas, NumPy, Scikit-learn, FastAPI, TextBlob, Cryptography
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Streaming & Processing: Apache Kafka, Apache Spark
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Containerization: Docker
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Deployment: AWS EC2, Kubernetes
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Social Media Platforms: Detects and reduces bot-generated content, ensuring authentic user engagement. 🌐🤖✨
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E-Commerce Websites: Identifies fake reviews and fraudulent activities to maintain trust. 🛒🔍💼
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News Portals: Prevents the spread of misinformation by flagging bot-generated news. 📰🚫⚡
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Financial Services: Protects trading platforms from automated fraudulent transactions. 💰📊🔐
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Government Agencies: Enhances cybersecurity by monitoring and mitigating bot-driven threats. 🏛️🛡️🔍
- Clone the Repository
git clone https://github.com/your-repo/bot-detection-system.git
cd bot-detection-system- Create Virtual Environment
python -m venv venv
source venv/bin/activate- Install dependencies
pip install -r requirements.txt- Start kafka Server
bin/zookeeper-server-start.sh config/zookeeper.properties
bin/kafka-server-start.sh config/server.properties- Run the API
uvicorn bot_detection_system:app --reloadContributions are always welcome!
See contributing.md for ways to get started.
Please adhere to this project's code of conduct.
This project is under MIT license.
Team Name: Byte Brigade
- Sagar Guney (Team Leader)
- Mayank Raj
- Harsh Raj
- Vikas Chaurasia