The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in...The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.展开更多
Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited late...Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited latency.DDoS attacks pose significant risks to entrepreneurial businesses,preventing legitimate customers from accessing their websites.These attacks require intelligent analytics before processing service requests.Distributed denial of service(DDoS)attacks exploit vulnerabilities in IoT devices by launchingmulti-point distributed attacks.These attacks generate massive traffic that overwhelms the victim’s network,disrupting normal operations.The consequences of distributed denial of service(DDoS)attacks are typically more severe in software-defined networks(SDNs)than in traditional networks.The centralised architecture of these networks can exacerbate existing vulnerabilities,as these weaknesses may not be effectively addressed in this model.The preliminary objective for detecting and mitigating distributed denial of service(DDoS)attacks in software-defined networks(SDN)is to monitor traffic patterns and identify anomalies that indicate distributed denial of service(DDoS)attacks.It implements measures to counter the effects ofDDoS attacks,and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats.The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks.The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.展开更多
Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not ...Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not in a time-efficient manner.The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks(SDNs)to achieve a better resource efficiency.This paper addresses this situation by combining co-training and Reinforcement Learning(RL)to enable a closed-loop classification approach that divides the entire classification process into episodes,each involving two elephant models.One predicts elephants and is retrained by a selection of flows automatically labeled online by the other.RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase.Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%,and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.展开更多
Zero Trust Network(ZTN)enhances network security through strict authentication and access control.However,in the ZTN,optimizing flow control to improve the quality of service is still facing challenges.Software Define...Zero Trust Network(ZTN)enhances network security through strict authentication and access control.However,in the ZTN,optimizing flow control to improve the quality of service is still facing challenges.Software Defined Network(SDN)provides solutions through centralized control and dynamic resource allocation,but the existing scheduling methods based on Deep Reinforcement Learning(DRL)are insufficient in terms of convergence speed and dynamic optimization capability.To solve these problems,this paper proposes DRL-AMIR,which is an efficient flow scheduling method for software defined ZTN.This method constructs a flow scheduling optimization model that comprehensively considers service delay,bandwidth occupation,and path hops.Additionally,it balances the differentiated requirements of delay-critical K-flows,bandwidth-intensive D-flows,and background B-flows through adaptiveweighting.Theproposed framework employs a customized state space comprising node labels,link bandwidth,delaymetrics,and path length.It incorporates an action space derived fromnode weights and a hybrid reward function that integrates both single-step and multi-step excitation mechanisms.Based on these components,a hierarchical architecture is designed,effectively integrating the data plane,control plane,and knowledge plane.In particular,the adaptive expert mechanism is introduced,which triggers the shortest path algorithm in the training process to accelerate convergence,reduce trial and error costs,and maintain stability.Experiments across diverse real-world network topologies demonstrate that DRL-AMIR achieves a 15–20%reduction in K-flow transmission delays,a 10–15%improvement in link bandwidth utilization compared to SPR,QoSR,and DRSIR,and a 30%faster convergence speed via adaptive expert mechanisms.展开更多
The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in...The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.展开更多
Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing...Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).展开更多
Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a ...Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.展开更多
Software-Defined Networking (SDN) has been a hot topic for future network development, which implements the different layers of control plane and data plane respectively. Despite providing high openness and programmab...Software-Defined Networking (SDN) has been a hot topic for future network development, which implements the different layers of control plane and data plane respectively. Despite providing high openness and programmability, the “three-layer two-interface” architecture of SDN changes the traditional network and increases the network attack nodes, which results in new security issues. In this paper, we firstly introduced the background, architecture and working process of SDN. Secondly, we summarized and analyzed the typical security issues from north to south: application layer, northbound interface, control layer, southbound interface and data layer. Another contribution is to review and analyze the existing solutions and latest research progress of each layer, mainly including: authorized authentication module, application isolation, DoS/DDoS defense, multi-controller deployment and flow rule consistency detection. Finally, a conclusion about the future works of SDN security and an idealized global security architecture is proposed.展开更多
Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhance...Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.展开更多
Elastic control could balance the distributed control plane in Software-Defined Networking(SDN). Dynamic switch migration has been proposed to achieve it. However, existing schemes mainly focus on how to execute migra...Elastic control could balance the distributed control plane in Software-Defined Networking(SDN). Dynamic switch migration has been proposed to achieve it. However, existing schemes mainly focus on how to execute migration operation, but not why. This paper designs a decision-making mechanism based on zero-sum game theory to reelect a new controller as the master for migrated switches. It first chooses a switch for migration in the heavy controller which invites its neighbors as the game players to compete for the master role of this switch in the game-playing field(GPF) which is an occasional and loose domain for game-playing. Second, based on the concept of GPF, we design a decentralized strategy to play the game and determine which player as the final master. We implement it by extending the Open Flow protocol. Finally, numerical results demonstrate that our distributed strategy can approach elastic control plane with better performance.展开更多
Software- defined networking （SDN） is a promising technology for next-generation networking and has attracted much attention from academics, network equipment manufacturer, network operators, and service providers. ...Software- defined networking （SDN） is a promising technology for next-generation networking and has attracted much attention from academics, network equipment manufacturer, network operators, and service providers. It has found center, and enterprise networks. applications in mobile, data The SDN architecture has a centralized, programmable control plane that is separate from the data plane. SDN also provides the ability to control and manage virtualized resources and networks without requiring new hardware technologies. This is a major shift in networking technologies.展开更多
The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the O...The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.展开更多
Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to...Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.展开更多
The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networ...The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.展开更多
Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection appr...Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.展开更多
Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic info...Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.展开更多
The low-cost,self-configuration capability and "plug-and-play" feature of Ethernet establishes its dominant position in the local area networks(LAN).However,it is hard to extend to large scale because of the...The low-cost,self-configuration capability and "plug-and-play" feature of Ethernet establishes its dominant position in the local area networks(LAN).However,it is hard to extend to large scale because of the legacy broadcast-based service discovery mechanism.Therefore,to solve this problem,a new split network architecture named Software-Defined Networking(SDN) is introduced in this paper,and a novel floodless service discovery mechanism(FSDM)for SDN is designed.For the FSDM,the widespread broadcast messages for Dynamic Host Configuration Protocol(DHCP) and Address Resolution Protocol(ARP) are considered especially,respectively.Then the DHCP relay and ARP proxy are proposed to handle DHCP broadcast messages and ARP broadcast messages,respectively.The proposed FSDM in this paper can eliminate flooding completely,reserve the autoconfiguration characteristics.Particularly,there is no need to change the existing hardware,software and protocols of hosts for the proposed scheme.Finally,the simulation results are demonstrated to show that our proposed model allows redundant links existed in network and has the property of scalability,which can significantly reduce network traffic in data plane and control traffic in control plane,and decrease the overhead of control plane.展开更多
Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,t...Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.展开更多
It is foreseen that the Internet of Things (IoT) will comprise billions of connected devices, and this will make the provi?sioning and operation of some IoT connectivity services more challenging. Indeed, IoT services...It is foreseen that the Internet of Things (IoT) will comprise billions of connected devices, and this will make the provi?sioning and operation of some IoT connectivity services more challenging. Indeed, IoT services are very different from lega?cy Internet services because of their dimensioning figures and also because IoT services differ dramatically in terms of na?ture and constraints. For example, IoT services often rely on energy and CPU?constrained sensor technologies, regardless of whether the service is for home automation, smart building, e?health, or power or water metering on a regional or national scale. Also, some IoT services, such as dynamic monitoring of biometric data, manipulation of sensitive information, and pri?vacy needs to be safeguarded whenever this information is for?warded over the underlying IoT network infrastructure. This paper discusses how software?defined networking (SDN) can facilitate the deployment and operation of some advanced IoT services regardless of their nature or scope. SDN introduces a high degree of automation in service delivery and operation-from dynamic IoT service parameter exposure and negotiation to resource allocation, service fulfillment, and assurance. This paper does not argue that all IoT services must adopt SDN. Rather, it is left to the discretion of operators to decide which IoT services can best leverage SDN capabilities. This paper only discusses managed IoT services, i.e., services that are op?erated by a service provider.展开更多
Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,w...Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.展开更多
基金supported in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the National Natural Science Foundation of China(Grant No.92067102)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘Distributed denial of service(DDoS)attacks are common network attacks that primarily target Internet of Things(IoT)devices.They are critical for emerging wireless services,especially for applications with limited latency.DDoS attacks pose significant risks to entrepreneurial businesses,preventing legitimate customers from accessing their websites.These attacks require intelligent analytics before processing service requests.Distributed denial of service(DDoS)attacks exploit vulnerabilities in IoT devices by launchingmulti-point distributed attacks.These attacks generate massive traffic that overwhelms the victim’s network,disrupting normal operations.The consequences of distributed denial of service(DDoS)attacks are typically more severe in software-defined networks(SDNs)than in traditional networks.The centralised architecture of these networks can exacerbate existing vulnerabilities,as these weaknesses may not be effectively addressed in this model.The preliminary objective for detecting and mitigating distributed denial of service(DDoS)attacks in software-defined networks(SDN)is to monitor traffic patterns and identify anomalies that indicate distributed denial of service(DDoS)attacks.It implements measures to counter the effects ofDDoS attacks,and ensure network reliability and availability by leveraging the flexibility and programmability of SDN to adaptively respond to threats.The authors present a mechanism that leverages the OpenFlow and sFlow protocols to counter the threats posed by DDoS attacks.The results indicate that the proposed model effectively mitigates the negative effects of DDoS attacks in an SDN environment.
基金supported by the National Natural Science Foundation of China(61962016)the Ministry of Science and Technology of China(G2022033002L)+1 种基金National Natural Science Foundation of Guangxi(2022JJA170057)Guangxi Education Department’s Project on Improving the Basic Research Ability of Young and Middleaged Teachers in Universities(2023ky0812,Research on Statistical Network Delay Predictions in Large-scale SDNs).
文摘Accurate early classification of elephant flows(elephants)is important for network management and resource optimization.Elephant models,mainly based on the byte count of flows,can always achieve high accuracy,but not in a time-efficient manner.The time efficiency becomes even worse when the flows to be classified are sampled by flow entry timeout over Software-Defined Networks(SDNs)to achieve a better resource efficiency.This paper addresses this situation by combining co-training and Reinforcement Learning(RL)to enable a closed-loop classification approach that divides the entire classification process into episodes,each involving two elephant models.One predicts elephants and is retrained by a selection of flows automatically labeled online by the other.RL is used to formulate a reward function that estimates the values of the possible actions based on the current states of both models and further adjusts the ratio of flows to be labeled in each phase.Extensive evaluation based on real traffic traces shows that the proposed approach can stably predict elephants using the packets received in the first 10% of their lifetime with an accuracy of over 80%,and using only about 10% more control channel bandwidth than the baseline over the evolved SDNs.
基金supported in part by Scientific Research Fund of Zhejiang Provincial Education Department under Grant Y202351110in part by Huzhou Science and Technology Plan Project under Grant 2024YZ23+1 种基金in part by Research Fund of National Key Laboratory of Advanced Communication Networks under Grant SCX23641X004in part by Postgraduate Research and Innovation Project of Huzhou University under Grant 2024KYCX50.
文摘Zero Trust Network(ZTN)enhances network security through strict authentication and access control.However,in the ZTN,optimizing flow control to improve the quality of service is still facing challenges.Software Defined Network(SDN)provides solutions through centralized control and dynamic resource allocation,but the existing scheduling methods based on Deep Reinforcement Learning(DRL)are insufficient in terms of convergence speed and dynamic optimization capability.To solve these problems,this paper proposes DRL-AMIR,which is an efficient flow scheduling method for software defined ZTN.This method constructs a flow scheduling optimization model that comprehensively considers service delay,bandwidth occupation,and path hops.Additionally,it balances the differentiated requirements of delay-critical K-flows,bandwidth-intensive D-flows,and background B-flows through adaptiveweighting.Theproposed framework employs a customized state space comprising node labels,link bandwidth,delaymetrics,and path length.It incorporates an action space derived fromnode weights and a hybrid reward function that integrates both single-step and multi-step excitation mechanisms.Based on these components,a hierarchical architecture is designed,effectively integrating the data plane,control plane,and knowledge plane.In particular,the adaptive expert mechanism is introduced,which triggers the shortest path algorithm in the training process to accelerate convergence,reduce trial and error costs,and maintain stability.Experiments across diverse real-world network topologies demonstrate that DRL-AMIR achieves a 15–20%reduction in K-flow transmission delays,a 10–15%improvement in link bandwidth utilization compared to SPR,QoSR,and DRSIR,and a 30%faster convergence speed via adaptive expert mechanisms.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.
基金supported by the National Natural Science Foundation of China for Innovative Research Groups (61521003)the National Natural Science Foundation of China (61872382)+1 种基金the National Key Research and Development Program of China (2017YFB0803204)the Research and Development Program in Key Areas of Guangdong Province (No.2018B010113001)
文摘Software-Defined Networking(SDN)adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources.However,due to the limitation of traditional routing strategies relying on manual configuration,SDN may suffer from link congestion and inefficient bandwidth allocation among flows,which could degrade network performance significantly.In this paper,we propose EARS,an intelligence-driven experiential network architecture for automatic routing.EARS adapts deep reinforcement learning(DRL)to simulate the human methods of learning experiential knowledge,employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment.The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions.Under the network architecture,we design the network utility function with throughput and delay awareness,differentiate flows based on their size characteristics,and design a DDPGbased automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows.To validate the network architecture,we implement it on a real network environment.Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes(e.g.OSPF,ECMP).
基金This work was supported by National Natural Science Foundation of China(No.61802080 and 61802077)Guangdong General Colleges and Universities Research Project(2018GkQNCX105)+1 种基金Zhongshan Public Welfare Science and Technology Research Project(2019B2044)Keping Yu was supported in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.
基金supported by the Wuhan Frontier Program of Application Foundation (No.2018010401011295)National High Technology Research and Development Program of China (“863” Program) (Grant No. 2015AA016002)
文摘Software-Defined Networking (SDN) has been a hot topic for future network development, which implements the different layers of control plane and data plane respectively. Despite providing high openness and programmability, the “three-layer two-interface” architecture of SDN changes the traditional network and increases the network attack nodes, which results in new security issues. In this paper, we firstly introduced the background, architecture and working process of SDN. Secondly, we summarized and analyzed the typical security issues from north to south: application layer, northbound interface, control layer, southbound interface and data layer. Another contribution is to review and analyze the existing solutions and latest research progress of each layer, mainly including: authorized authentication module, application isolation, DoS/DDoS defense, multi-controller deployment and flow rule consistency detection. Finally, a conclusion about the future works of SDN security and an idealized global security architecture is proposed.
文摘Software-defined networking(SDN)is an innovative paradigm that separates the control and data planes,introducing centralized network control.SDN is increasingly being adopted by Carrier Grade networks,offering enhanced networkmanagement capabilities than those of traditional networks.However,because SDN is designed to ensure high-level service availability,it faces additional challenges.One of themost critical challenges is ensuring efficient detection and recovery from link failures in the data plane.Such failures can significantly impact network performance and lead to service outages,making resiliency a key concern for the effective adoption of SDN.Since the recovery process is intrinsically dependent on timely failure detection,this research surveys and analyzes the current literature on both failure detection and recovery approaches in SDN.The survey provides a critical comparison of existing failure detection techniques,highlighting their advantages and disadvantages.Additionally,it examines the current failure recovery methods,categorized as either restoration-based or protection-based,and offers a comprehensive comparison of their strengths and limitations.Lastly,future research challenges and directions are discussed to address the shortcomings of existing failure recovery methods.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.61521003)the National Basic Research Program of China(2012CB315901,2013CB329104)+2 种基金the National Natural Science Foundation of China(Grant No.61372121,61309020,61309019)the National High-Tech Research&Development Program of China(Grant No.2013AA013505)the National Science and Technology Support Program Project(Grant No.2014BAH30B01)
文摘Elastic control could balance the distributed control plane in Software-Defined Networking(SDN). Dynamic switch migration has been proposed to achieve it. However, existing schemes mainly focus on how to execute migration operation, but not why. This paper designs a decision-making mechanism based on zero-sum game theory to reelect a new controller as the master for migrated switches. It first chooses a switch for migration in the heavy controller which invites its neighbors as the game players to compete for the master role of this switch in the game-playing field(GPF) which is an occasional and loose domain for game-playing. Second, based on the concept of GPF, we design a decentralized strategy to play the game and determine which player as the final master. We implement it by extending the Open Flow protocol. Finally, numerical results demonstrate that our distributed strategy can approach elastic control plane with better performance.
文摘Software- defined networking （SDN） is a promising technology for next-generation networking and has attracted much attention from academics, network equipment manufacturer, network operators, and service providers. It has found center, and enterprise networks. applications in mobile, data The SDN architecture has a centralized, programmable control plane that is separate from the data plane. SDN also provides the ability to control and manage virtualized resources and networks without requiring new hardware technologies. This is a major shift in networking technologies.
基金This research was supported partially by LIG Nex1It was also supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning Evaluation).
文摘The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61521003)The National Key R&D Program of China (No.2016YFB0800101)+1 种基金the National Science Foundation for Distinguished Young Scholars of China (No.61602509)Henan Province Key Technologies R&D Program of China(No.172102210615)
文摘Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.
基金supported in part by National Natural Science Foundation of China (No.61401331,No.61401328)111 Project in Xidian University of China(B08038)+2 种基金Hong Kong,Macao and Taiwan Science and Technology Cooperation Special Project (2014DFT10320,2015DFT10160)The National Science and Technology Major Project of the Ministry of Science and Technology of China(2015zx03002006-003)FundamentalResearch Funds for the Central Universities (20101155739)
文摘The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.
文摘Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.
基金supported by the National Postdoctoral Science Foundation of China(2014M550068)
文摘Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines（VMs）. This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.
基金supported by the National Basic Research Program(973)of China(No.2012CB315801)the National Natural Science Fund(No.61302089,61300184)the fundamental research funds for the Central Universities(No.2013RC0113)
文摘The low-cost,self-configuration capability and "plug-and-play" feature of Ethernet establishes its dominant position in the local area networks(LAN).However,it is hard to extend to large scale because of the legacy broadcast-based service discovery mechanism.Therefore,to solve this problem,a new split network architecture named Software-Defined Networking(SDN) is introduced in this paper,and a novel floodless service discovery mechanism(FSDM)for SDN is designed.For the FSDM,the widespread broadcast messages for Dynamic Host Configuration Protocol(DHCP) and Address Resolution Protocol(ARP) are considered especially,respectively.Then the DHCP relay and ARP proxy are proposed to handle DHCP broadcast messages and ARP broadcast messages,respectively.The proposed FSDM in this paper can eliminate flooding completely,reserve the autoconfiguration characteristics.Particularly,there is no need to change the existing hardware,software and protocols of hosts for the proposed scheme.Finally,the simulation results are demonstrated to show that our proposed model allows redundant links existed in network and has the property of scalability,which can significantly reduce network traffic in data plane and control traffic in control plane,and decrease the overhead of control plane.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金the Hainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.
文摘It is foreseen that the Internet of Things (IoT) will comprise billions of connected devices, and this will make the provi?sioning and operation of some IoT connectivity services more challenging. Indeed, IoT services are very different from lega?cy Internet services because of their dimensioning figures and also because IoT services differ dramatically in terms of na?ture and constraints. For example, IoT services often rely on energy and CPU?constrained sensor technologies, regardless of whether the service is for home automation, smart building, e?health, or power or water metering on a regional or national scale. Also, some IoT services, such as dynamic monitoring of biometric data, manipulation of sensitive information, and pri?vacy needs to be safeguarded whenever this information is for?warded over the underlying IoT network infrastructure. This paper discusses how software?defined networking (SDN) can facilitate the deployment and operation of some advanced IoT services regardless of their nature or scope. SDN introduces a high degree of automation in service delivery and operation-from dynamic IoT service parameter exposure and negotiation to resource allocation, service fulfillment, and assurance. This paper does not argue that all IoT services must adopt SDN. Rather, it is left to the discretion of operators to decide which IoT services can best leverage SDN capabilities. This paper only discusses managed IoT services, i.e., services that are op?erated by a service provider.
文摘Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.
