Intrusion Detection and Prevention in Wireless Sensor Networks

Abstract

This Research Introduces An Innovative Approach, The Enhanced Anomaly-Based Intrusion Detection And Prevention (Abid) System, Designed Specifically For Wireless Sensor Networks (Wsns). Leveraging Advanced Machine Learning Techniques, The Proposed Methodology Involves Meticulous Data Collection, Feature Extraction, And Model Training On Normal Behavior. Through Comprehensive Testing And Parameter Tuning, The System Achieves Optimal Performance, Effectively Detecting And Preventing Intrusions. The Validation Process On Diverse Datasets Demonstrates The System's Robust Generalization Capabilities. The Enhanced Abid System Not Only Addresses The Dynamic Nature Of Wsn Environments But Also Provides A Scalable And Adaptable Solution To Enhance The Overall Cybersecurity Of Sensor Networks. This Research Contributes Valuable Insights To The Evolving Landscape Of Intrusion Detection And Prevention In Wsns, Paving The Way For Enhanced Security Measures In The Realm Of The Internet Of Things (Iot).