Anomaly Detection in Website Traffic

This project aims to develop a robust anomaly detection system for website traffic data. By leveraging advanced machine learning techniques, the system can effectively identify unusual patterns, outliers, and potential threats that may impact website performance and user experience.

Key Features:

Data Ingestion: Efficiently collects and processes website traffic data from various sources.
Feature Engineering: Extracts relevant features from raw data for effective anomaly detection.
Machine Learning Models: Implements state-of-the-art machine learning algorithms (e.g., Autoencoder, LSTM, Isolation Forest) to detect anomalies.
Model Training and Evaluation: Trains and evaluates models on historical data to ensure accuracy and performance.

Technologies Used:

Programming Language: Java
Framework: Spring Boot
Machine Learning Library: Weka
Database: SQL/H2
Web Framework: Thymeleaf/Bootstrap (for frontend)
Deployment: Docker (for containerization)

Potential Applications:

Fraud Detection: Identify fraudulent activities like bot attacks or click fraud.
Performance Monitoring: Detect performance issues and bottlenecks.
Security Threats: Identify potential security breaches or unauthorized access.
User Behavior Analysis: Understand user patterns and identify unusual behaviors.

This project can be valuable for website owners, developers, and security professionals who want to proactively monitor and protect their websites from various anomalies.