• Asha Sisodiya
• Dr. Ganpat joshi

In today's world, where digital threats to network security are constantly changing, the need for advanced intrusion detection systems is more important than ever. Traditional systems often face challenges like high rates of false positives and sluggish response times, which can lead to inefficiencies that jeopardize sensitive information and the integrity of the entire network. Tackling these problems is crucial, especially in fields like healthcare, where safeguarding patient data is vital. Fortunately, recent breakthroughs in machine learning have brought forth some exciting solutions, enhancing detection capabilities by utilizing algorithms like Support Vector Machines and K-Nearest Neighbors. By weaving these techniques into a software-defined networking framework, the proposed model seeks to streamline real-time threat detection and response, ultimately boosting overall system performance.

Network security, Machine learning and Intrusion detection.

"A STUDY OF REAL TIME INTRUSION DETECTION FRAMEWORK FOR NETWORK SECURITY USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.149-153, June-2025, Available :http://www.jetir.org/papers/JETIRGW06025.pdf

"A STUDY OF REAL TIME INTRUSION DETECTION FRAMEWORK FOR NETWORK SECURITY USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp149-153, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06025.pdf