• Ms. Devika K
• Dr. Shashidhar Kini K

As network technologies evolve, traditional Intrusion Detection Systems (IDS) struggle to detect advanced threats like APTs and zero-day attacks. This project presents a machine learning-based IDS using Random Forest and Gradient Boosting to analyze real-time network traffic, improve detection accuracy, and reduce false positives. Designed for scalability and adaptability, it suits modern environments such as cloud, IoT, and enterprise networks. The system also incorporates future-ready features like explainable AI, federated learning, and support for edge computing and 5G, offering a smart, efficient, and reliable network security solution.

Network security, Cyber threats, Machine learning algorithms, Intrusion detection system, Zero-day attacks, anomaly detection.

"DRIVEN INTRUSION DETECTION SYSTEM FOR NETWORK SECURITY USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1137-1141, July-2025, Available :http://www.jetir.org/papers/JETIRGX06218.pdf

"DRIVEN INTRUSION DETECTION SYSTEM FOR NETWORK SECURITY USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1137-1141, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06218.pdf