为了充分发挥校园网为学校的教学、科研、信息交流、协同工作的作用,对多个校区的网络互连进行了研究,比较了几种VPN技术,提出了利用GRE over IPSec VPN技术实现多校区网络互连,同时又能保证内部保密数据安全地网络中进行传输。设计了...为了充分发挥校园网为学校的教学、科研、信息交流、协同工作的作用,对多个校区的网络互连进行了研究,比较了几种VPN技术,提出了利用GRE over IPSec VPN技术实现多校区网络互连,同时又能保证内部保密数据安全地网络中进行传输。设计了网络拓扑结构,构建了网络实验平台,配置了GRE over IPSec VPN,最终对配置的结果进行了验证。为多校区校园网互连提供了一种安全的解决方案。展开更多
人类社会已经进入21世纪，计算机信息网络已深入到世界的各个角落，地域、国家、政府、企业甚至家庭。计算机网络的飞速发展给人来带来了诸多便利，而然其潜在的网络信息安全威胁也弥漫在各个领域。探讨的VPN技术则是建立在GRE over IP...人类社会已经进入21世纪，计算机信息网络已深入到世界的各个角落，地域、国家、政府、企业甚至家庭。计算机网络的飞速发展给人来带来了诸多便利，而然其潜在的网络信息安全威胁也弥漫在各个领域。探讨的VPN技术则是建立在GRE over IPSec技术之上，并通过合肥百大集团的网络拓扑对其进行设计与仿真。GREoverIPSecVPN技术是通过GRE与IPSec相结合，而形成的一种安全性更好VPN技术，其主要借用IPSec的安全加密和GRE支持多播的优点，从而使得VPN网络更加安全。该项技术的主要工作原理：将一个完整的组播、广播数据包或非IP数据包封装在一个单播数据包（IPSEC）里，以处理如OSPF的组播或RIP的广播数据流，以完成在IPSec隧道里通信实体之间的动态路由学习。展开更多
虚拟专用网(Virtual Private Network,VPN)技术提供了一种通过公用网络安全地对企业内部专用网络进行远程访问的连接方式.本文采用GNS3虚拟仿真软件,设计了适用于LAN-to-LAN的VPN实验案例.分析了GRE over IPSec和NAT技术,利用Wireshark...虚拟专用网(Virtual Private Network,VPN)技术提供了一种通过公用网络安全地对企业内部专用网络进行远程访问的连接方式.本文采用GNS3虚拟仿真软件,设计了适用于LAN-to-LAN的VPN实验案例.分析了GRE over IPSec和NAT技术,利用Wireshark工具进行了协议分析,并给出了实验结果.经过实践表明,利用GNS3虚拟实验平台进行实验教学,提高了实验教学效果,该实验增强了学生对GRE over IPSec VPN的理解能力和实践能力.展开更多
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
本文使用GRE over IPSec技术,为分布在3个城市的某大型设计公司解决了其内网的跨区域互联问题,使其所使用的动态路由协议OSPF在基于IPSec的GRE隧道中安全传输,并在分公司的防火墙上使用了路由重发布技术,实现了该公司全网互通、数据安...本文使用GRE over IPSec技术,为分布在3个城市的某大型设计公司解决了其内网的跨区域互联问题,使其所使用的动态路由协议OSPF在基于IPSec的GRE隧道中安全传输,并在分公司的防火墙上使用了路由重发布技术,实现了该公司全网互通、数据安全传输。展开更多
文摘为了充分发挥校园网为学校的教学、科研、信息交流、协同工作的作用,对多个校区的网络互连进行了研究,比较了几种VPN技术,提出了利用GRE over IPSec VPN技术实现多校区网络互连,同时又能保证内部保密数据安全地网络中进行传输。设计了网络拓扑结构,构建了网络实验平台,配置了GRE over IPSec VPN,最终对配置的结果进行了验证。为多校区校园网互连提供了一种安全的解决方案。
文摘人类社会已经进入21世纪，计算机信息网络已深入到世界的各个角落，地域、国家、政府、企业甚至家庭。计算机网络的飞速发展给人来带来了诸多便利，而然其潜在的网络信息安全威胁也弥漫在各个领域。探讨的VPN技术则是建立在GRE over IPSec技术之上，并通过合肥百大集团的网络拓扑对其进行设计与仿真。GREoverIPSecVPN技术是通过GRE与IPSec相结合，而形成的一种安全性更好VPN技术，其主要借用IPSec的安全加密和GRE支持多播的优点，从而使得VPN网络更加安全。该项技术的主要工作原理：将一个完整的组播、广播数据包或非IP数据包封装在一个单播数据包（IPSEC）里，以处理如OSPF的组播或RIP的广播数据流，以完成在IPSec隧道里通信实体之间的动态路由学习。
文摘虚拟专用网(Virtual Private Network,VPN)技术提供了一种通过公用网络安全地对企业内部专用网络进行远程访问的连接方式.本文采用GNS3虚拟仿真软件,设计了适用于LAN-to-LAN的VPN实验案例.分析了GRE over IPSec和NAT技术,利用Wireshark工具进行了协议分析,并给出了实验结果.经过实践表明,利用GNS3虚拟实验平台进行实验教学,提高了实验教学效果,该实验增强了学生对GRE over IPSec VPN的理解能力和实践能力.
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.
