A survey on software defined networking with multiple controllers

Software-Defined Networking (SDN) enhances the flexibility and programmability of networks by separating control plane and data plane. The logically centralized control mechanism makes the control plane vulnerable in both single and multiple controller scenarios. Malicious third parties can exploit vulnerabilities of reactive forwarding mode to launch distributed denial-of-service (DDoS) attacks against SDN controllers. Unfortunately, existing DoS/DDoS solutions under single controller can not afford effective performance under multiple controllers due to the absence of cooperative detection and mitigation. To solve the above problem, we propose a blockchain-based SDN-targeted DDoS defense framework (BSD-Guard) that can provide cooperative detection and mitigation mechanism to protect SDN controllers. BSD-Guard introduces a blockchain-based secure middle plane between control plane and data plane. The secure middle plane calculates the suspect rate of new flows based on the collected packets’ information and reports suspect lists to blockchain for immutably storing and sharing. Besides, the smart contract deployed on blockchain in advance constitutes collaborative defense strategies based on the suspect lists reported from multiple SDN domains. When receiving defense strategies, the secure middle plane converts them to specific flow table actions and installs actions into relevant switches. The experimental results indicate that BSD-Guard can efficiently detect DoS/DDoS attacks in multiple controllers scenario and issue precise defensive strategies near the source of attack by identifying the attack path.

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

The nature of computer network flash crowd traffic, which is generated by legitimate users accessing servers or other network resources are similar to the traffic generated by Distributed Denial of Service (DDoS) like attacks. With advancement in spoof packet generation tools, attacker may generate Multi-source Multi-destination Multi-protocol (MMM) traffic; characteristics of such traffic are very similar to on-going genuine/ flash crowd traffic in the network. In the case of Software Defined Network (SDN), attacker’s target is controller plane. Controller plane in SDN is a centralized processing unit of the underlying network, which manages several data planes. Controller plane frames the policies and pushes forwarding rules to the data planes. Data planes just maintain the forwarding rules. Thus by overloading the SDN controller, functionality of complete computer network will be hampered. In this paper, we have proposed Media Access Control (MAC) address based Model to Differentiate Flash crowd and Malicious traffic in SDN (MDFMS). Novelty of the proposed model is to detect, locate and mitigate the source of Traditional DDoS (T-DDoS) and MMMDDoS traffic. MDFSM has been implemented on separate machine to avoid any additional computing load on SDN controller. It also preserves the original design of the SDN architecture. Proposed model has been evaluated under various scenarios and encouraging results have been obtained to differentiate T-DDoS and MMM-DDoS from benign flash crowd traffic.

Software Defined Network (SDN) Technology made tremendous changes in networking field by controlling the hardware with remote software by decoupling data plane and control plane. The focus here is on the controller which is part of control plane. A controller is a central part of SDN; it is an intermediary between Data plane and Forwarding plane. It takes prime place in SDN architecture like Operating System in Computers. SDN Controller is a software which directs the packets using flow rules and also contains statistical information of network. The controller orchestrates the networking devices such as switches and routers by channelizing the packets. SDN controller has different flavor based on the commercial requirement achieved by including new pluggable module. This paper discusses some of the SDN Controller with their feature; the controllers are POX, NOX, Floodlight, ODL, Open Contrail. This paper also compares the characteristic of centralized and distributed controller by focusing on performance, bandwidth and round-trip time of Floodlight and Open Daylight (ODL) controller. The simulation is conducted using Mininet.

The traditional networks, with the control and data plane integrated into the same network devices, do not provide a global view of the network performance like degree of congestion, bandwidth utilization, etc. Software defined network (SDN) is an approach towards this problem which separates the control plane of the switch from its data plane and provide a centralized control plane so as to get a global view of the network performance and thus make decisions of how to regulate flows. In SDN, network monitoring can be achieved more efficiently than traditional networks using OpenFlow statistics. SDN controller can keep track of available bandwidth on each link and thus estimate end-to-end available bandwidth of a path simply by composing individual link bandwidths thus avoiding end-to-end probing. We have made two contributions in this paper: (i) proposed and validated a method to estimate end-to-end available bandwidth on any given path by composing link-wise available bandwidths (ii) proposed a method to measure link capacity using OpenFlow protocol. We compared our results to the ones obtained using the state-of-the-art bandwidth measurement tool, Yaz.

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

In the fast moving space of technology, everything and everyone is on the Internet accessing the world. The creation of large networks and data centers required for managing and analyzing data will increase in the future, so traditional networking technologies and skills will not be enough to solve the future complications arising in the networking industry. The present networks are more static and rigid where else they need to be more dynamic and flexible. Software Defined Networking (SDN) pledges to solve these issues and change the status quo of the industry. The key ideas behind Software Defined Networking are the separation of control and data plane, centralization of network policies and programmability of the networks. Software Defined Networking also claims to solve the perplexing network management issues with some related technologies. This paper provides an insight into this new paradigm. In the inception, we introduce Software Defined Networking and what exactly led to SDN. The fundamental difference between SDN and traditional networking is discussed next. Then, we discuss the different definitions of SDN proposed by different communities covering every aspect of this new paradigm namely: standardization efforts, history, architectural framework with three planes of networking, northbound and southbound interfaces, SDN controllers, and network applications. We also take a look at the ongoing research work especially from the eye of industry giants like Microsoft, Google, AT&T, etc. specifically in the field of deployment and interoperability among different carrier vendors and cloud service providers. We conclude the paper by explaining how SDN has brought the whole ecosystem under one roof.

Software Defined Networking (SDN) has emerged as a new networking paradigm that is based on the decoupling between data plane and control plane providing several benefits that include flexible, manageable, and centrally controlled networks. From a security point of view, SDNs suffer from several vulnerabilities that are associated with the nature of communication between control plane and data plane. In this context, software defined networks are vulnerable to distributed denial of service attacks. In particular, the centralization of the SDN controller makes it an attractive target for these attacks because overloading the controller with huge packet volume would result in bringing the whole network down or degrade its performance. Moreover, DDoS attacks may have the objective of flooding a network segment with huge traffic volume targeting single or multiple end systems. In this paper, we propose an entropy-based mechanism for Distributed Denial of Service (DDoS) attack detection and mitigation in SDN networks. The proposed mechanism is based on the entropy values of source and destination IP addresses of flows observed by the SDN controller which are compared to a preset entropy threshold values that change in adaptive manner based on network dynamics. The proposed mechanism has been evaluated through extensive simulation experiments.

Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of upmu text {s} in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language.Graphic

Software-defined networking (SDN) has revolutionized traditional network management by decoupling the control and data planes, offering enhanced flexibility, programmability, and efficiency. However, this centralized architecture makes SDN vulnerable to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, which severely impact network performance and reliability. Through overloading critical network components, especially the SDN controller, these attacks reduce network availability and result in significant service interruptions. This review offers a thorough summary of mitigation strategies designed to combat DoS and DDoS attacks in SDN systems. These attacks overwhelm network resources, disrupt services, and compromise the availability of critical components such as the SDN controller. Additionally, it explores the key challenges facing SDN in defending against DoS and DDoS attacks, such as the need for real-time detection, the impact of network scale, and the complexities of adaptive responses. As such, addressing DoS and DDoS attacks has become a critical research area for ensuring the security and robustness of SDN-based networks. It categorizes existing approaches based on detection methods, response strategies, and deployment models, providing a detailed analysis of their strengths and limitations. Furthermore, it discusses problems such as scalability, concurrent detection, and adaptability that hinder the effectiveness of current solutions. Emerging trends, including integrating artificial intelligence, blockchain, and collaborative frameworks, are highlighted as promising directions for future research. This aim is to give researchers, network admins, and cyber-security practitioners an insight into the state of the art as well as a basis on which to build for future SDN security advancements. This survey aims to serve as a comprehensive resource for researchers and practitioners seeking to imporve SDN's resilience against DoS and DDoS attacks.

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.

Network administrators employ a variety of security policies to protect the data and services. However, the lack of standard control protocol and distributed network control causes these policies to be complicated in traditional networks. An ideal paradigm is provided by Software Defined Network (SDN) to address these challenges through separating the control plane from data plane. Security policies are enforced by logically centralized control's which take advantage of SDN. In this paper, we present a fully configurable system for an institution using POX which is a famous SDN controller. A network administrator can enforce the policies related to security, access and control through it. So that a single change in policy will be reflected in all the OpenFlow switches, attached to the SDN, resulting in reduced cost and time, as compare to the conventional networks where each switch is managed individually. The purpose of this research is to achieve a secure and controllable SDN by blocking the malicious and restricting the anonymous users in the network.

Software-defined network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and reprogrammability of the networks. From a security perspective, SDN separates security concerns into control and data plane, and this architectural recomposition brings up exciting opportunities and challenges. The overall perception is that SDN capabilities will ultimately result in improved security. However, in its raw form, SDN could potentially make networks more vulnerable to attacks and harder to protect. In this paper, we provide a comprehensive review of SDN security domain while focusing on its data plane, which is one of the least explored but most critical aspects in securing this technology. We review the most recent enhancements in SDNs, identify the main vulnerabilities of SDNs, and provide a novel attack taxonomy for SDNs. Thereafter, we provide a comprehensive analysis of challenges involved in protecting SDN data plane and control plane and provide an in-depth look into available solutions with respect to the identified threats and identify their limitations. To highlight the importance of securing the SDN platform, we also review the numerous security services built on top of this technology. We conclude the paper by offering future research directions.

New and emerging use cases, such as the interconnection of geographically distributed data centers (DCs), are drawing attention to the requirement for dynamic end-to-end service provisioning, spanning multiple and heterogeneous optical network domains. This heterogeneity is, not only due to the diverse data transmission and switching technologies, but also due to the different options of control plane techniques. In light of this, the problem of heterogeneous control plane interworking needs to be solved, and in particular, the solution must address the specific issues of multi-domain networks, such as limited domain topology visibility, given the scalability and confidentiality constraints. In this article, some of the recent activities regarding the Software-Defined Networking (SDN) orchestration are reviewed to address such a multi-domain control plane interworking problem. Specifically, three different models, including the single SDN controller model, multiple SDN controllers in mesh, and multiple SDN controllers in a hierarchical setting, are presented for the DC interconnection network with multiple SDN/ OpenFlow domains or multiple OpenFlow/ Generalized Multi-Protocol Label Switching (GMPLS) heterogeneous domains. In addition, two concrete implementations of the orchestration architectures are detailed, showing the overall feasibility and procedures of SDN orchestration for the end-to-end service provisioning in multi-domain data center optical networks.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.