Hybrid Machine Learning And Deep Learning-based Vpn Network Traffic Anomaly Detection System

Abstract

The Rapid Growth Of Internet Usage And The Widespread Adoption Of Virtual Private Networks (VPNs) Have Significantly Enhanced Secure Communication, But Have Also Introduced New Challenges In Detecting Cyber Threats Hidden Within Encrypted Traffic. Traditional Intrusion Detection Systems Often Fail To Identify Such Threats Due To Their Reliance On Signature-based Methods And Inability To Analyze Encrypted Payloads Effectively. This Project Presents A Hybrid Deep Learning-based VPN Traffic Detection System That Integrates Convolutional Neural Networks (CNN) And Long Short-Term Memory (LSTM) Networks To Accurately Identify Anomalous Network Behavior. The System Utilizes Structured Network Traffic Data With Features Such As Protocol Type, Service, Flag Status, Traffic Rates, And Byte Counts, Which Are Preprocessed Using Label Encoding And Feature Scaling Techniques. In Addition, Conventional Machine Learning Models Such As Random Forest And Support Vector Machine (SVM) Are Implemented For Performance Comparison. The Hybrid CNN–LSTM Model Captures Both Spatial And Temporal Patterns In Network Traffic, Resulting In Improved Detection Accuracy. A Real-time Web-based Dashboard Developed Using Streamlit Enables Users To Input Parameters And Visualize Prediction Results, Including Classification Outcomes And Confidence Scores. An Automated Alert Mechanism Is Also Incorporated To Notify Users Of Suspicious Activities, Facilitating Timely Response To Potential Threats. Experimental Results Demonstrate That The Proposed System Outperforms Traditional Methods In Terms Of Accuracy, Precision, And Recall, Providing A Scalable And Efficient Solution For Real-time VPN Traffic Monitoring And Cyber-attack Detection In Enterprise Environments.

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