Edge Computing Security: Advanced Feature Selection Adopted Supervised Learning Models for Real-Time Intrusion Detection

Abstract

The proliferation of edge computing has introduced new challenges in ensuring robust securitymechanisms against potential cyber threats. This project focuses on enhancing real-time intrusiondetection in edge computing environments through the adoption of advanced feature selectiontechniques and supervised learning models. The existing system employs a K-Nearest Neighbors(KNN) Classifier for intrusion detection, demonstrating satisfactory performance but with limitationsconcerning processing time and prediction accuracy.