Integrating Ai Tools for Real-Time Anomaly Detection in Cloud Vpns: A Case of Owncloud.

Abstract

The increasing reliance on cloud-based Virtual Private Networks (VPNs) has significantly improved the security and scalability of digital infrastructures. However, the increasing complexity of these systems introduced new challenges in ensuring their security, particularly in detecting anomalies such as unauthorized access, abnormal traffic, and data breaches in real time. This research addressed the problem of inadequate anomaly detection in the dynamic cloud VPN environments by investigating the integration of Artificial Intelligence (AI) tools to enhance real-time threat detection, using OwnCloud as a case study. The research aimed to identify effective AI models for anomaly detection, develop a real-time AI-based prototype, and evaluate its performance in detecting anomalies within cloud VPN traffic. Both supervised and unsupervised machine learning techniques were explored, including Isolation Forest and Long Short-Term Memory (LSTM) models. Simulated VPN traffic data was generated using Mininet, and Apache Kafka was employed to stream this data in real time to a Spark-based AI detection engine. Anomaly detection outputs were logged and visualized using the Kibana dashboard, while alerts were configured to trigger based on spikes and deviations from normal traffic patterns. The prototype demonstrated the feasibility of AI-based tools in identifying unknown and evolving threats more effectively than traditional signature-based systems. Unlike conventional methods that rely on historical data and static thresholds, the AI-driven system adapted to emerging threat patterns and significantly reduced false positives. The comparative analysis of AI models confirmed that the hybrid (LSTM + Isolation Forest) model was the most effective AI-based approach for anomaly detection in simulated cloud VPN traffic. It not only delivered superior performance metrics but also demonstrated adaptability in real-world scenarios where labeled anomalies are scarce, and encrypted traffic restricts payload inspection. The model recorded the highest Precision of 0.94, Recall of 0.91, F1-Score of 0.92, and Accuracy of 0.93. The developed AI-based prototype system effectively achieved real-time anomaly detection in OwnCloud VPN traffic. Its hybrid architecture, based on LSTM and IF, delivered accurate, timely, and interpretable results, hence validating its potential for integration into real-world cloud security systems. The ROC curve for the real-time anomaly detection prototype revealed exceptional performance, with an AUC score of 0.98 confirming its effectiveness in distinguishing between normal and anomalous traffic. The findings highlighted the potential of AI to improve the responsiveness and accuracy of intrusion detection mechanisms in cloud-based environments. In conclusion, the research successfully demonstrated that AI tools can enhance real-time anomaly detection in the cloud VPNs, offering improved threat response and reduced false alarms. This research will add to the existing knowledge of AI integration to improve the security of cloud VPNs by exploring a case study in real life. It is recommended that future implementations expand on this approach by integrating more advanced deep learning models, refining real-time alert systems, and applying the solution to diverse cloud platforms to further validate scalability and robustness.