VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and c...VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.展开更多
文摘VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.
