The objective of this project is to design and develop a Network Anomaly Detection System integrated with a trust scoring model. The system monitors network traffic to identify abnormal activities and dynamically evaluates trust levels, helping students understand network security, anomaly detection techniques, and risk-based decision making.
Study computer network fundamentals and common network security threats.
Analyze types of network anomalies such as DoS attacks, unusual traffic spikes, and unauthorized access attempts.
Prepare Software Requirement Specification (SRS) and network monitoring workflow documentation.
Design system architecture including traffic monitoring module, anomaly detection engine, and trust evaluation layer.
Create database schema for network logs, detected anomalies, trust scores, alerts, and user/device records.
Implement network traffic data collection using simulated or real packet logs.
Develop anomaly detection logic using rule-based thresholds (BCA level) or basic machine learning models (MCA level).
Calculate trust scores for users or devices based on detected anomalies and traffic behavior.
Dynamically update trust levels when suspicious activities are detected.
Generate alerts and notifications for low-trust or high-risk network events.
Develop dashboard to visualize network traffic patterns, anomalies, and trust scores.
Maintain detailed audit logs for all detected events and trust updates.
Apply input validation and secure coding practices in log processing.
Perform unit testing and system testing for anomaly detection accuracy.
Simulate attack scenarios and evaluate trust score response.
Prepare documentation including network diagrams, anomaly detection logic explanation, ER diagrams, and test cases.
Deploy the system locally and demonstrate real-time or near real-time monitoring.
