LPMLP-based framework for IPsec VPN cloud gateway with advanced network monitoring and issue resolution
Nowadays, VPN technology is widely used in cloud and hybrid network communication that makes use of algorithms and tunneling to meet different security requirements. However, existing cloud VPN gateways often lack advanced monitoring capabilities and struggle to identify and resolve network connectivity and performance issues. Hence, LPMLP adapted Secure cloud VPN Gateway with Network Monitoring and Issue Resolution is proposed. Here, the VPN raw log data is taken as input and pre-processed. Then, Apache Spark is used to handle the big unstructured data. The patterns are extracted from the structured data using the KMP technique. From the patterns, correlation using PCC is computed and events are scored based on the correlation value. Next, RADFCM clustering is used to group the correlated anomalies. Based on the clustered result, the SPBERT summarizer is used to summarize the issues in the network. In the meantime, time series features are extracted from the structured data, and optimal features are selected via the RLCEPO technique. On the other side, the metrics data are captured and they undergo preprocessing followed by PCC-based correlation analysis. Finally, the summarized issues, selected features, along with correlated anomaly metrics are given to the LPMLP classifier to perform the recommendation. The experimental evaluation proved the efficiency of the proposed framework.