ADAPTIVE MACHINE LEARNING FOR LOW-OVERHEAD ANOMALY DETECTION IN RESIDENTIAL NETWORKS: A COMPREHENSIVE SECURITY FRAMEWORK

Abstract

This article presents a novel framework for enhancing residential network security through machine learning-enhanced anomaly detection, addressing the growing challenges of protecting home networks in an increasingly connected environment. The framework is specifically designed for deployment on residential gateways, functioning as an edge-based security solution that provides comprehensive protection without requiring additional hardware. The proposed system incorporates lightweight machine learning algorithms for resource-constrained environments, enabling real-time threat detection while maintaining minimal system overhead. The framework employs a hybrid approach combining unsupervised learning techniques with adaptive detection mechanisms, allowing for automated pattern recognition and evolution with changing network behaviors. Through comprehensive experimental validation across diverse home network environments, the article demonstrates that the approach successfully identifies potential security threats while maintaining low false-positive rates and computational efficiency. The results indicate significant improvements in detection accuracy compared to traditional methods while requiring minimal technical expertise from end-users. This article contributes to the field by introducing a practical, scalable solution for residential network security that balances sophisticated threat detection capabilities with resource efficiency and user accessibility. The findings suggest promising applications for enhancing cybersecurity in smart home environments and provide a foundation for future research in adaptive network security systems.