NetShield: A User-Centric Deep Learning Framework for Real-Time Network Anomaly Detection and Resolution

Abstract

The growing dependence on digital networks has made network anomalies like performance degradation and critical security threats more disruptive. Crucially, existing detection systems are often too technical and lack practical guidance for average users. This study addresses that gap by developing a machine learning-based framework that detects a wide range of network anomalies and notifies users with simple, actionable solutions. The aim was to validate a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to ensure effective detection. The model was trained and evaluated on five diverse benchmark datasets, enhancing robustness and generalizability. The results showed that the model achieved F1-scores above 97% across all datasets, outperforming traditional machine learning approaches. A fully functional prototype was developed to convert these outputs into real-time notifications, offering step-by-step guidance. For instance, upon detecting a phishing attempt, the system can automatically block the site and advise the user to never enter credentials on suspicious links. This research provides a validated, user-friendly framework that bridges the gap between technical anomaly detection and everyday cybersecurity practices, empowering users to take an active role in protecting their digital environments.