Privacy Information
This project is developed as part of a master's thesis titled "Leveraging Artificial Intelligence to Analyze Customer Behavior on Websites: A Predictive Model for Fraud Detection, Fake Accounts, and Security Breaches Mitigation." It aims to utilize AI-driven techniques to analyze user behavior on various websites, including e-commerce platforms, news sites, and other online services. By analyzing activity patterns, the predictive model will help detect fraud, identify fake accounts, and mitigate potential security breaches.
What We Collect
Our system collects and analyzes user behavior data, including: Browsing patterns: Clickstream data, page visits, and navigation history Interaction data: Time spent on pages, form submissions, and login activities Anomaly detection: Identifying unusual login attempts or transaction activities User metadata: Device information, IP addresses, and geolocation (if applicable) All collected data is processed in compliance with privacy policies and ethical AI principles.
Help
If you need assistance regarding the system, please refer to the documentation or contact the project team. The system is designed to analyze customer behavior using artificial intelligence and machine learning techniques. For inquiries about implementation, configuration, or troubleshooting, reach out via email or support channels.
About
This project integrates artificial intelligence and cybersecurity techniques to enhance online security. The core objectives include: Fraud detection: Identifying suspicious activities and fraudulent transactions Fake account detection: Recognizing and filtering out automated or fraudulent user accounts Security breach mitigation: Predicting and preventing cyber threats based on real-time analysis The system is built using ASP.NET Core, Microsoft SQL Server (MSSQL), ML.NET, Python (TensorFlow, PyTorch), and ELK Stack (Elasticsearch, Logstash, Kibana).
Other Information
Key Technologies Used: Artificial Intelligence (AI) Machine Learning (ML.NET, TensorFlow, PyTorch) Cybersecurity & Fraud Detection Data Analysis & Predictive Modeling E-commerce and Web Security ELK Stack for real-time log monitoring