Fraudulent Ad Detection and Honeypot Engagement System: Advanced Fraud Prevention with Machine Learning
Introduction
Social media platforms like Facebook and Instagram are increasingly targeted by fraudsters who create deceptive ads to lure users into providing personal information. These fraudulent ads often direct users to forms where they submit their contact details, after which they are contacted by scammers via phone or WhatsApp. The scammers then attempt to convince victims to install remote desktop applications and perform unauthorized bank transfers.
To combat this, we propose a comprehensive Fraudulent Ad Detection and Honeypot Engagement System. This system not only identifies and interacts with scammers using honeypot techniques but also employs advanced machine learning algorithms to automatically detect potentially fraudulent ads before users fall victim. By integrating real-time reporting and collaboration with law enforcement and financial institutions, this system represents a robust defense against online scams.
System Overview
1. Ad Monitoring and Data Collection
Web Scraping and Ad Monitoring
- Automated Scraping: The system utilizes BeautifulSoup and Selenium to continuously scrape Facebook and Instagram for ads that link to forms. It captures ad content, including text, images, and metadata, for analysis.
- Business Manager Integration: Integration with the Facebook Business Manager API enables the system to monitor and analyze ads in real-time, focusing on specific campaigns or keywords associated with high-risk activities.
Machine Learning-Based Ad Fraud Detection
- Dataset and Model Training: The system is trained on a labeled dataset of known fraudulent and legitimate ads. This dataset includes historical data on scam ads, which have been manually labeled, and ads identified as safe. The training process uses TensorFlow to build a deep learning model capable of distinguishing between fraudulent and non-fraudulent ads based on their content and metadata.
- Real-Time Ad Scoring: As new ads are detected, they are analyzed by the machine learning model, which assigns a fraud probability score. Ads with a high probability of being fraudulent are flagged for further investigation and potential honeypot engagement.
Form Auto-Filling (Honeypot Activation)
- Honeypot Form Submission: For ads flagged as suspicious by the machine learning model, the system automatically fills out the associated form using pre-configured honeypot data. This setup is designed to attract follow-up contact from scammers.
2. Honeypot Engagement
Monitoring and Interaction
- Automated Response System: Upon being contacted by the scammer, the honeypot’s automated response system is activated. It uses pre-programmed dialogues to interact with the scammer, guiding the conversation toward eliciting critical information such as bank account details.
- Voice and Text Communication: The system manages both voice (via VoIP and transcription) and text interactions (using chatbots powered by Rasa). Conversations are designed to mimic genuine interest and compliance with the scammer’s requests, leading them to reveal more details.
Bank Account and Phone Number Identification
- Bank Account Exposure: The primary goal during engagement is to have the scammer provide a bank account number for the transfer. This bank account is immediately flagged in the system for further action.
- Phone Number Tracking: The phone numbers used by the scammer are logged and analyzed. Any new phone numbers are automatically added to a blacklist, and cross-referenced with existing data to identify patterns or repeat offenders.
3. Real-Time Reporting and Law Enforcement Coordination
Dynamic Dashboard
- Centralized Reporting: The system features a real-time dashboard, powered by Tableau, providing a comprehensive view of all flagged ads, honeypot interactions, blacklisted phone numbers, and identified bank accounts. This dashboard is accessible to authorized personnel, including security teams and investigators.
- Interactive Visualization: The dashboard offers interactive visualizations, such as timelines, heatmaps, and geographic distributions of scam activity, allowing for in-depth analysis and quick identification of trends.
Automated Alerts and Reporting
- Real-Time Notifications: Integrated with Twilio, the system sends real-time SMS and email alerts to law enforcement and financial institutions when a bank account or phone number is flagged. These alerts include detailed logs of the honeypot interaction and the fraud score of the ad.
- Secure API Integration: The system is connected via secure APIs to law enforcement databases and bank fraud detection systems, ensuring that critical data is shared instantly for prompt action, such as freezing the fraudulent accounts.
Example Workflow
- Ad Detection and Analysis: The system detects a new ad on Instagram promoting a suspicious offer. The ad is analyzed by the machine learning model, which assigns it a high fraud probability score.
- Honeypot Form Submission: The system automatically fills out the linked form with honeypot data, setting up the scenario for engagement.
- Scammer Contact: The scammer contacts the honeypot via WhatsApp, requesting a bank transfer after a series of interactions.
- Engagement and Data Collection: The honeypot engages the scammer through automated responses, eventually obtaining the scammer’s bank account details. The phone number used by the scammer is also logged and flagged.
- Real-Time Alert and Reporting: The flagged bank account and phone number are reported to law enforcement and the bank via real-time alerts. The incident is also documented in the live dashboard for further analysis.
- Dashboard Update: The dashboard updates in real-time, showing the new incident and providing access to detailed logs, including conversation transcripts and fraud analysis metrics.
Tools and Technologies
1. Machine Learning and Data Analysis
- TensorFlow: Used to develop and train a deep learning model that accurately detects fraudulent ads based on historical labeled data. This model is critical for the real-time classification of new ads as they are detected.
- BeautifulSoup and Selenium: Tools for web scraping and real-time monitoring of Facebook and Instagram ads, ensuring all relevant content is captured for analysis.
2. Honeypot Interaction
- Rasa Chatbot Framework: Powers the automated text interactions, guiding scammers through a pre-defined script to extract critical information.
- Google Speech-to-Text API: Transcribes voice interactions in real-time, enabling the system to process and analyze conversations with scammers efficiently.
3. Real-Time Reporting and Alerts
- Tableau: Provides a powerful and interactive dashboard for monitoring all aspects of the system, from flagged ads to honeypot engagements.
- Twilio: Integrated for immediate alerting, ensuring that law enforcement and financial institutions are notified as soon as potential fraud is detected.
4. Secure API Integration
- API Connections: Secure integration with law enforcement and bank systems allows for the rapid sharing of flagged bank accounts and phone numbers, ensuring swift action to prevent further fraud.
Best Practices
1. Continuous Model Improvement: Regularly update and retrain the machine learning model with new data to improve its ability to detect evolving scam tactics. This includes incorporating feedback from false positives and negatives into the training dataset.
2. Ethical Honeypot Usage: Ensure the honeypot interactions are conducted ethically, with clear guidelines to avoid entrapment or legal issues. Data collected should be handled in compliance with privacy laws.
3. Collaboration with Stakeholders: Maintain strong communication channels with law enforcement and financial institutions to ensure that flagged accounts are acted upon quickly, reducing the risk of successful scams.
4. Real-Time Response Capabilities: The system’s real-time capabilities are crucial for preventing financial loss. Ensure that all components, from ad monitoring to alerting, operate with minimal latency.
Conclusion
The Fraudulent Ad Detection and Honeypot Engagement System represents a comprehensive approach to combating online fraud. By combining machine learning for ad fraud detection with proactive honeypot engagement, this system not only identifies potential scams but actively works to expose scammers, gather critical evidence, and enable swift action by law enforcement and financial institutions.
This advanced solution not only protects users from falling victim to scams but also disrupts fraudulent networks, making digital platforms safer for all users. By leveraging cutting-edge technology and best practices, this system sets a new standard in fraud prevention and online security.