Abstract: As cloud-based applications continue to grow in popularity, safeguarding virtual network access has emerged as a major cybersecurity concern. This study introduces a Cloud-Based Virtual Network Traffic Monitoring Framework that strengthens security by tracking and evaluating both inbound and outbound traffic within a web application environment. The system records comprehensive traffic logs during each user login session and securely stores them in the cloud infrastructure. To detect unauthorized access, it leverages a combination of machine learning models: autoencoders for unsupervised pattern recognition and logistic regression for supervised classification. This dual-model strategy enables the system to effectively understand typical access behaviors and flag anomalies. Upon identifying an unauthorized IP address, the system blocks further access attempts from that source in real time. By automating access control and anomaly detection, the framework enhances protection against cyber threats while aligning with Zero Trust Architecture principles. This proactive security solution serves as a critical asset for organizations striving to defend their virtual networks in cloud environments.
Keywords: Autoencoder, Cloud Security, Logistic Regression, Network Traffic Monitoring, Unauthorized Access.
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DOI:
10.17148/IJARCCE.2025.14618
