A Multi-Level Machine Learning–Based Intrusion Detection System for IoT Networks

Key Points

• This research aims to develop and evaluate a multi-level machine learning-based system to improve security in IoT networks.
• Developed a multi-level machine learning model to detect intrusions in IoT networks.
• Evaluated model performance using real-time data and benchmark datasets for accuracy and response time.
• Implemented various algorithms for comparison in detecting different types of network intrusions.
• Achieved a detection accuracy of 95% with the proposed system.
• Reduced false positive rates significantly, improving security alerts' reliability.
• Demonstrated faster detection speeds compared to traditional systems, with an average response time under 2 seconds.