Machine Learning-Based Network Intrusion Detection System for Real-Time Traffic Monitoring

Objective: To design and implement a real-time network intrusion detection system leveraging machine learning for identifying malicious traffic in enterprise environments.

Real-world problem: Indian enterprises, including SMEs, educational institutions, and government organizations, face increasing cyber threats such as malware, ransomware, and unauthorized access due to limited cybersecurity infrastructure and skilled personnel. Existing traditional rule-based intrusion detection systems (IDS) often fail to detect novel or sophisticated attacks, leading to data breaches and service disruptions.

Proposed solution: This project aims to develop a machine learning-based IDS that analyzes live network traffic from a real LAN using packet capture tools. The system will extract relevant features, apply trained ML models for classification, and generate real-time alerts for detected anomalies or attacks. The solution includes traffic collection, preprocessing, model training, deployment, and dashboard-based alerting.

Key deliverables: The working model will demonstrate live packet capture on a test network, feature extraction, real-time inference using a selected ML algorithm (e.g., Random Forest or SVM), and visualization of detected intrusions. The system will be validated using the CICIDS2017 dataset and tested against simulated attacks (e.g., port scans, brute force, DoS) to prove effectiveness. Deliverables include source code, deployed prototype, system documentation, a user manual, and a research paper.

Industry/societal impact: The IDS enhances organizational security posture by providing early detection of advanced threats, reducing incident response time and potential damages. The scalable and modular architecture supports integration with existing SIEM tools, making it suitable for deployment in Indian SMEs and educational networks. Successful implementation can be extended to larger networks and inform local cybersecurity standards.