An Efficient Intrusion Detection System Using Advanced Machine Learning Techniques in SDN for Healthcare System.

Area of Science:

• Computer Science
• Cybersecurity
• Health Informatics

Background:

• Healthcare systems face significant security challenges due to sensitive patient data and the need for uninterrupted service.
• Current intrusion detection systems (IDS) struggle with high false positive rates, poor accuracy, slow response times, and scalability issues.
• These limitations lead to ineffective threat management, wasted resources, and compromised healthcare network security.

Purpose of the Study:

• To propose an efficient and real-time intrusion detection system (IDS) tailored for healthcare networks.
• To leverage advanced machine learning techniques within a software-defined networking (SDN) framework for enhanced security.
• To improve threat detection, response capabilities, and overall network efficiency in healthcare environments.

Main Methods:

• Developed a novel IDS architecture integrating machine learning models, specifically a hybrid Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) approach.
• Utilized a software-defined networking (SDN) framework to enable dynamic threat processing and control over network operations.
• Implemented comprehensive detection and mitigation capabilities designed for various network traffic types with minimal interference.

Main Results:

• The proposed hybrid SVM-KNN model demonstrated a 30% overall advancement in detection and mitigation compared to traditional models.
• Achieved significant performance improvements: 20-30% less CPU usage, 30-50% reduction in end-to-end delay, 30-40% lower latency, and 20-40% less propagation delay.
• Exhibited 20-30% better prediction accuracy, outperforming Fuzzy, Logistic Regression, and Decision Tree methods.

Conclusions:

• The integrated SVM-KNN and SDN approach provides a robust and efficient solution for healthcare network security.
• The proposed model effectively addresses the limitations of current IDS, offering superior threat detection, faster response, and improved network performance.
• This advancement is crucial for safeguarding sensitive patient records and ensuring the reliability of healthcare services.