Project Overview This project is focused on the design and deployment of an Intrusion Detection System (IDS) aimed at enhancing network security. The IDS leverages advanced data analysis techniques and machine learning algorithms for effective pattern recognition and anomaly detection. Additionally, it features a user-friendly interface for real-time monitoring and management of security alerts. This deep learning project was a group project led by me.
Features
1.)Advanced Data Analysis: Utilizes sophisticated data analysis methods to detect unusual patterns and potential security threats.
2.)Machine Learning Algorithms: Implements machine learning algorithms to recognize patterns and anomalies in network traffic, improving detection accuracy over time.
3.)User-Friendly Interface: Provides an intuitive interface for users to monitor network activity and manage security alerts efficiently.
Components
1.)Data Collection Module: Collects network traffic data for analysis.
2.)Analysis Engine: Processes the collected data using predefined rules and machine learning models to identify potential threats.
3.)Alert Management System: Generates alerts for detected anomalies and provides tools for managing and responding to these alerts.
4.)User Interface: A dashboard for real-time monitoring of network activity and alerts