🔍 CryptoTokenFraudAnalyzer: AI-Powered Telegram Bot for Token Risk Analysis

🚀 Overview

CryptoTokenFraudAnalyzer is an intelligent Telegram bot that provides comprehensive risk analysis of cryptocurrency tokens by combining:

• Smart contract analysis (Etherscan/BscScan API)
• Market data (CoinGecko API)
• News sentiment analysis (Web scraping + LLM)
• AI-powered insights (Google Gemini)
• Machine learning risk scoring (XGBoost)

📽 Project Presentations

• Main Project Presentation

✨ Enhanced Key Features

🔬 Deep Token Analysis

• Contract verification status
• Detection of suspicious functions (mint, pause, blacklist)
• Market volatility indicators
• CEX/DEX listing status
• Historical price dump detection
• News sentiment analysis

📰 News Analysis Module

• Real-time news aggregation from crypto sources
• LLM-powered sentiment scoring (Positive/Neutral/Negative)
• Key event detection (partnerships, hacks, regulations)

🤖 AI-Powered Reporting

• Natural language reports generated by Gemini
• Clear risk assessment (Safe/Scam) with news impact factor
• Telegram-friendly formatting with emoji visualization

📊 ML Risk Scoring

• XGBoost model with news features
• Binary classification (scam/not_scam)
• Dynamic weight adjustment based on news
• Confidence scoring system

🛠 Updated Technical Implementation

graph TD
    A[User Request] --> B[Telegram Bot]
    B --> C{Input Type}
    C -->|Contract Address| D[Blockchain Analysis]
    C -->|Token Symbol| E[Market Data Lookup]
    D --> F[Feature Extraction]
    E --> F
    F --> G[News Aggregation]
    G --> H[LLM Sentiment Analysis]
    F --> I[ML Risk Scoring]
    H --> I
    F --> J[Gemini Report Generation]
    I --> J
    J --> K[Final Report]
    K --> L[User Delivery]

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📦 Installation

1. Clone the repository:

git clone https://github.com/vaskers5/crypto_token_fraud_analyzer
cd crypto_token_fraud_analyzer

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Fill in your API keys

4. Run the bot:

python main.py

🌟 Project Highlights

Academic Innovations

• Multi-modal analysis combining technical, market and news data
• Novel news impact scoring algorithm
• Dynamic risk assessment model
• Comprehensive validation framework

📚 Documentation

Component	Description	Tech Used
Data Collector	Fetches blockchain and market data	Etherscan, CoinGecko
Feature Engine	Extracts risk indicators	Pandas, NumPy
ML Model	Predicts scam probability	XGBoost, Scikit-learn
AI Reporter	Generates natural language reports	Google Gemini
Telegram UI	User interaction interface	python-telegram-bot

📈 Performance Metrics

Metric	Score
Prediction Accuracy	87.1%
Report Generation	<7s