Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. ...Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. One enabling technology for wireless SDN is network virtualization, which logically divides one wireless network element, such as a base station, into multiple slices, and each slice serving as a standalone virtual BS. In this way, one physical mobile wireless network can be partitioned into multiple virtual networks in a software-defined manner. Wireless virtual networks comprising virtual base stations also need to provide QoS to mobile end-user services in the same context as their physical hosting networks. One key QoS parameter is delay. This paper presents a delay model for software-defined wireless virtual networks. Network calculus is used in the modelling. In particular, stochastic network calculus, which describes more realistic models than deterministic network calculus, is used. The model enables theoretical investigation of wireless SDN, which is largely dominated by either algorithms or prototype implementations.展开更多
Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a ...Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.展开更多
Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks...Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.展开更多
随着信息技术的不断发展,网络在各个领域的重要性日益凸显。高可靠的网络传输成为确保各类业务正常运行的关键。基于IPv6的段路由(Segment Routing over IPv6,SRv6)作为一种新兴的网络技术,为实现高可靠网络传输带来了新的契机。基于SRv...随着信息技术的不断发展,网络在各个领域的重要性日益凸显。高可靠的网络传输成为确保各类业务正常运行的关键。基于IPv6的段路由(Segment Routing over IPv6,SRv6)作为一种新兴的网络技术,为实现高可靠网络传输带来了新的契机。基于SRv6的原理和特点,研究提出了实现高可靠网络传输的技术途径,分析了面临的问题挑战,列举了相关应用场景,旨在为推动高可靠网络传输和网络可编程技术的发展提供有益的参考。展开更多
针对基于真实流量的空、天、地、海异构波形组网验证需求,提出了一种基于容器的天地一体化软件定义网络(Software Defined Network,SDN)半实物组网验证架构,开展了面向SDN的异构子网组网能力验证,突破了异构网虚-实接口技术、基于真实...针对基于真实流量的空、天、地、海异构波形组网验证需求,提出了一种基于容器的天地一体化软件定义网络(Software Defined Network,SDN)半实物组网验证架构,开展了面向SDN的异构子网组网能力验证,突破了异构网虚-实接口技术、基于真实流量的组网仿真技术和模拟干扰应用环境组网验证技术。通过实验表明,该技术虚-实接口接入速率可达到物理总线最高传输速率90%以上,仿真网络可承载真实流量,能有效实现模拟应用环境的异构波形半实物组网验证,具有良好的可扩展性。展开更多
空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(netw...空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(network functions virtualization,NFV)的空天地一体化网络任务部署与恢复,首先阐述了空天地一体化网络系统架构,介绍了各层网络构成、SDN和NFV原理及其相关应用,然后,针对上述挑战,以服务功能链技术为抓手,提出了面向任务的服务功能链优化部署、利用智能算法实现动态调度、通过匹配博弈算法完成失效恢复等策略,最后,构建了一个用例,设定节点部署、服务功能链建模等,验证了所提策略在提升服务功能链完成效率以及应对资源故障方面的有效性,旨在为空天地一体化网络资源管理提供理论基础。展开更多
随着信息通信技术的飞速发展,下一代通信网络(如5G/6G)对网络性能提出了更高的要求,特别是在低延迟、高带宽、海量设备接入和智能化管控等方面。文章分析了软件定义网络(Software Defined Network,SDN)在大带宽、低时延和大规模物联网...随着信息通信技术的飞速发展,下一代通信网络(如5G/6G)对网络性能提出了更高的要求,特别是在低延迟、高带宽、海量设备接入和智能化管控等方面。文章分析了软件定义网络(Software Defined Network,SDN)在大带宽、低时延和大规模物联网环境中的应用,提出了协议优化策略并采用理论建模与仿真实验相结合的方法,评估不同优化方案的效果。结果表明:SDN优化能有效降低网络延迟,提高带宽利用率,增强物联网设备管理能力。展开更多
网络流量分类在网络管理和安全中至关重要,尤其是精准识别分布式拒绝服务(Distributed Denial of Service,DDoS)攻击这一威胁。DDoS攻击会导致服务中断、资源耗尽和经济损失,严重影响服务质量(QoS)。尽管集中式模型在DDoS攻击检测中取...网络流量分类在网络管理和安全中至关重要,尤其是精准识别分布式拒绝服务(Distributed Denial of Service,DDoS)攻击这一威胁。DDoS攻击会导致服务中断、资源耗尽和经济损失,严重影响服务质量(QoS)。尽管集中式模型在DDoS攻击检测中取得了一定成效,但在实际应用中存在挑战:数据分布不均、数据集中传输困难,以及异构设备和动态网络环境的限制,从而难以实现实时检测。为应对这些问题,本文提出了一种基于异步个性化联邦学习的DDoS攻击检测与缓解方法AdaPerFed(Adaptive Personalized Federated Learning)。首先,通过定制的ResNet架构高效处理一维流量数据,并集成Net模块增强特征提取能力。然后,通过软件定义网络(SDN,Software-Defined Networking)模拟复杂动态网络环境,并引入完善的缓解系统应对多样化攻击场景。个性化联邦学习框架有效处理了非独立同分布(Non-IID,Non-Independent and Identically Distributed)数据问题,并通过异步学习机制适应异构设备和网络条件的差异,提升了系统的鲁棒性和扩展性。实验结果表明,AdaPerFed在CICDDoS2019、CIC-IDS2017和InSDN等数据集上均优于其他联邦学习算法,在不同客户端数量下展现出更快的收敛速度和更强的鲁棒性,DDoS检测准确率提升了15%~20%。消融实验进一步验证了个性化聚合模块对系统性能的显著提升。展开更多
传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(S...传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(Software Defined Networking,SDN),本文提出了一种基于LLM的轻量级自动化配置方法.在数据平面,提出了一种基于检索增强生成(Retrieval-Augmented Generation,RAG)技术的代码自动生成方法RetroP4,支持基于用户意图生成P4代码;在控制平面,提出了一种基于任务分解的流表自动生成方法CtrlSynth,支持基于用户意图和数据平面P4代码生成流表配置.实验结果表明:相较于通用大模型,RetroP4生成的P4代码的语法正确性提高了25%,语义正确性提高了87.5%;CtrlSynth能够准确生成与P4代码匹配的流表信息,在流量意图不超过300条时,准确率可达100%.展开更多
基金supported in part by the grant from the National Natural Science Foundation of China (60973129)
文摘Software-defined networks （SDN） have attracted much attention recently because of their flexibility in terms of network management. Increasingly, SDN is being introduced into wireless networks to form wireless SDN. One enabling technology for wireless SDN is network virtualization, which logically divides one wireless network element, such as a base station, into multiple slices, and each slice serving as a standalone virtual BS. In this way, one physical mobile wireless network can be partitioned into multiple virtual networks in a software-defined manner. Wireless virtual networks comprising virtual base stations also need to provide QoS to mobile end-user services in the same context as their physical hosting networks. One key QoS parameter is delay. This paper presents a delay model for software-defined wireless virtual networks. Network calculus is used in the modelling. In particular, stochastic network calculus, which describes more realistic models than deterministic network calculus, is used. The model enables theoretical investigation of wireless SDN, which is largely dominated by either algorithms or prototype implementations.
基金Supported by the National Natural Science Foundation of China(61373040,61173137)the Ph.D.Programs Foundation of Ministry of Education of China(20120141110002)the Key Project of Natural Science Foundation of Hubei Province(2010CDA004)
文摘Software defined networking（SDN） has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.
文摘Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.
文摘随着信息技术的不断发展,网络在各个领域的重要性日益凸显。高可靠的网络传输成为确保各类业务正常运行的关键。基于IPv6的段路由(Segment Routing over IPv6,SRv6)作为一种新兴的网络技术,为实现高可靠网络传输带来了新的契机。基于SRv6的原理和特点,研究提出了实现高可靠网络传输的技术途径,分析了面临的问题挑战,列举了相关应用场景,旨在为推动高可靠网络传输和网络可编程技术的发展提供有益的参考。
文摘针对基于真实流量的空、天、地、海异构波形组网验证需求,提出了一种基于容器的天地一体化软件定义网络(Software Defined Network,SDN)半实物组网验证架构,开展了面向SDN的异构子网组网能力验证,突破了异构网虚-实接口技术、基于真实流量的组网仿真技术和模拟干扰应用环境组网验证技术。通过实验表明,该技术虚-实接口接入速率可达到物理总线最高传输速率90%以上,仿真网络可承载真实流量,能有效实现模拟应用环境的异构波形半实物组网验证,具有良好的可扩展性。
文摘空天地一体化网络作为6G技术的关键组成,在整合天基、空基和地基网络时,面临节点异构性、业务多样性等挑战,进而引发资源分配、竞争及故障风险等问题。基于此,聚焦基于软件定义网络(software defined network,SDN)与网络功能虚拟化(network functions virtualization,NFV)的空天地一体化网络任务部署与恢复,首先阐述了空天地一体化网络系统架构,介绍了各层网络构成、SDN和NFV原理及其相关应用,然后,针对上述挑战,以服务功能链技术为抓手,提出了面向任务的服务功能链优化部署、利用智能算法实现动态调度、通过匹配博弈算法完成失效恢复等策略,最后,构建了一个用例,设定节点部署、服务功能链建模等,验证了所提策略在提升服务功能链完成效率以及应对资源故障方面的有效性,旨在为空天地一体化网络资源管理提供理论基础。
文摘随着信息通信技术的飞速发展,下一代通信网络(如5G/6G)对网络性能提出了更高的要求,特别是在低延迟、高带宽、海量设备接入和智能化管控等方面。文章分析了软件定义网络(Software Defined Network,SDN)在大带宽、低时延和大规模物联网环境中的应用,提出了协议优化策略并采用理论建模与仿真实验相结合的方法,评估不同优化方案的效果。结果表明:SDN优化能有效降低网络延迟,提高带宽利用率,增强物联网设备管理能力。
文摘传统网络依赖人工配置,在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时,效率低下且成本高昂.大语言模型(Large Language Model,LLM)凭借其出色的自然语言理解能力,在网络自动化配置中展现出巨大的潜力.面向软件定义网络(Software Defined Networking,SDN),本文提出了一种基于LLM的轻量级自动化配置方法.在数据平面,提出了一种基于检索增强生成(Retrieval-Augmented Generation,RAG)技术的代码自动生成方法RetroP4,支持基于用户意图生成P4代码;在控制平面,提出了一种基于任务分解的流表自动生成方法CtrlSynth,支持基于用户意图和数据平面P4代码生成流表配置.实验结果表明:相较于通用大模型,RetroP4生成的P4代码的语法正确性提高了25%,语义正确性提高了87.5%;CtrlSynth能够准确生成与P4代码匹配的流表信息,在流量意图不超过300条时,准确率可达100%.
