SDN Controller Research Articles

Exploring current solutions against DDoS attacks in SDN environment

Software Defined Networking (SDN) offers a novel approach to network management, with the potential to simplify administration and enhance security. However, as with any emerging technology, it comes with vulnerabilities that may impact its availability and functionality. A prevalent attack on SDN controllers is Distributed Denial of Service (DDoS), which, while not entirely preventable, can have its effects mitigated. This study explores the factors influencing the performance of industry solutions in defending SDN against DDoS attacks and evaluates their efficiency. It investigates various security challenges associated with DDoS attacks in both Information Technology (IT) and Operational Technology (OT) environments and contrasts the effects of distinct DDoS attack types on multiple levels of SDN communication, such as Northbound-Southbound and East-West. Lastly, the applicable solution’s effectiveness was assessed based on the controller’s hardware utilization and response time.

An Effective Deployment of Controllers in Software-Defined Networks

Network performance has become a crucial consideration in ensuring a flawless user experience in the era of data-intensive applications and cloud computing. Traditional Networks are not suitable for solving such issues, so Software-defined networking (SDN) has evolved as a game-changing technology, promising network flexibility, scalability, and centralized control. However, some challenges remain open to the research, and one of the most popularly known challenges is controller problem placement (CPP). Latency is also a major consideration in the network. However, implementing low-latency communication in SDN systems remains a difficult issue, especially as networks become more complicated. In the realm of SDN, where centralized control meets distributed data planes, minimizing latency between switches and controllers emerges as a pivotal challenge. This article delves into the critical concept of optimizing controller placement within SDN networks to minimize latency, with a primary focus on considering the switch-controller distance. The proposed work uses a Linear programming-based approach to identify the optimal controller location in the network by considering the switch-controller distance and the latency is calculated. Further, a comparison of the proposed work with a heuristic-based approach is performed which shows that the proposed work performs well in terms of latency.

Network Architecture and Security Assessment in Industry 4.0 for Smart Manufacturing

Abstract In this paper, we propose a centralized SDN network architecture under a smart factory for the problems under the traditional industrial IoT architecture and carry out the security analysis of industrial IoT to improve the performance of the smart factory network. Briefly describing the industrial SDN network architecture, combined with the design of a blockchain system for smart factories, the hash algorithm and asymmetric encryption algorithm are introduced to anonymize and protect the data. For traditional SDN, propose a blockchain-based SDN and analyze the probability of state transfer between SDNs. In order to solve the security problem at the SDN control level, the blockchain-based distributed SDN control model is proposed, and the PBFT consensus algorithm is introduced to realize the blockchain consensus-based multi-controller decision-making model. Add the MD5 algorithm and compare its performance with the SHA256 algorithm. Set up Adder modules with different bit widths and CSA Tree modules with different bases to calculate the encryption time of the RSA algorithm for various combinations. Set up simulation experiments to compare different malicious nodes and the average trust value of the consensus mechanism proposed in this paper for abnormal nodes in an SDN multi-controller. From the results, it can be seen that the computational speed of the RSA system is the fastest for the system implemented by Montgomery’s algorithm in base 16 and bit width 32 bits, and the time to complete the encryption process of 1024 bits once is only 0.97 ms. However, when the number of nodes is controlled in [0~5000], the rejection rate of abnormal nodes by the PBFT consensus algorithm in this paper basically maintains at about 92%. The consensus node designed and proposed in this paper is able to reject error nodes effectively and effectively secure the SDN smart factory network architecture.

Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI, and MTD Approaches to Security Solutions

Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI, and MTD Approaches to Security Solutions

MFFLR-DDoS: An encrypted LR-DDoS attack detection method based on multi-granularity feature fusions in SDN.

Low rate distributed denial of service attack (LR-DDoS) is a special type of distributed denial of service (DDoS) attack, which uses the vulnerability of HTTP protocol to send HTTP requests to applications or servers at a slow speed, resulting in long-term occupation of server threads and affecting the normal access of legitimate users. Since LR-DDoS attacks do not need to send flooding or a large number of HTTP requests, it is difficult for traditional intrusion detection methods to detect such attacks, especially when HTTP traffic is encrypted. To overcome the above problems, we proposed an encrypted LR-DDoS attack detection and mitigation method based on the multi-granularity feature fusion (MFFLR-DDoS) for software defined networking (SDN). This method analyzes the encrypted session flow from the time sequence of packets and the spatiality of session flow and uses different deep learning methods to extract features, to obtain more effective features for abnormal traffic detection. In addition, we used the advantages of SDN architecture to perform real-time defense against LR-DDoS attacks by the way of SDN controller issuing flow rules. The experimental results showed that the MFFLR-DDoS model had a higher detection rate than advanced methods, and could mitigate LR-DDoS attack traffic online and in real-time.

Open-Source Software Defined Networking Controllers: State-of-the-Art, Challenges and Solutions for Future Network Providers

Open-Source Software Defined Networking Controllers: State-of-the-Art, Challenges and Solutions for Future Network Providers

A comparative analysis of software-defined network controllers in terms of network forensics processes and capabilities

A comparative analysis of software-defined network controllers in terms of network forensics processes and capabilities

Advanced greedy hybrid bio-inspired based shortest path routing algorithm for SDN controller over VANET: Issues and challenges

Advanced greedy hybrid bio-inspired based shortest path routing algorithm for SDN controller over VANET: Issues and challenges

Optimizing IIoT Performance: Intelligent Selection of SDN Controllers through AHP Analysis

Optimizing IIoT Performance: Intelligent Selection of SDN Controllers through AHP Analysis

Impact of SDN Controller’s Performance on Quality of Service

Impact of SDN Controller’s Performance on Quality of Service

Multi Controller Placement Optimization in Software Defined Network Controllers by using Genetic Algorithm

SDN stands for software-defined network, and it gives scalability to manage the network by giving the concept of centralization and open control. The key concept of software-defined networks is that it separates the control mechanism and data mechanism, so the controller of SDN is only responsible for controlling the network functions. The controller can be programmed or decoded as per the need of the network administrator, whether the network administrator wants to expand the network or downscale the existing network. Open flow is the protocol which is used to build communication between controllers and a switching node. Open flow protocol is responsible for synchronizing and receiving the flow table when the request from the switches increases to control the level and multiple routing information comes to the controller. The controller could not be able to manage such an overflow of information. As a result, the performance of the controller was affected. In this paper genetic algorithm technique is used to resolve the load balancing issue between the multiple controllers. The proposed solution of algorithm will perform the method of selection, crossover, and mutation techniques to optimize the load balancing of controllers. The proposed technique will be compared with some benchmark algorithms, and it will be proved that the proposed GA outperforms as compared to other existing algorithms. By optimizing network performance and resource utilization, the research contributes to building resilient infrastructure and promoting sustainable industrialization.

Improved Performance of 5G Based Software Defined Networks

With the growth of processed data for wireless networks and the establishment of new applications and services, mobile operators keep searching for solutions to deal with the issues raised by nextgeneration networks (NGN). The fifth generation (5G) mobile networks, for example, should enhance their performance without consuming a lot of energy. For these reasons, software-defined networks (SDN) appear to be the promising technology that will make achieving the architectural agility required for the upcoming 5G mobile networks easier. SDN is a clever solution for providing innovation and enforcing the primary drivers in 5G mobile networks, such as flexibility, dependability, service-oriented management, and cost reduction through the control and management of the 5G core network. In this paper, the integration of SDN and multiple controllers with the 5G core network is investigated to determine how these two technologies can affect mobile IP network performance. An energy model suitable for the proposed network architecture has been developed. A widely used Diamond network is being considered for the core network. Open-Flow controllers were used to improve the routing performance by considering load balancing of IP traffic. According to such a model, reduced energy consumption is experienced with SDN. The addition of an SDN controller results in a 11% reduction in consumed energy. The results also show that the use of SDN and multi-controller with NGN can improve the performance further. Using SDN reduced the average delay of the network by 14%. A great reduction in packet error rate (PER) is also achieved when SDN is used.

Coalitional Game Strategy and TSSM for Efficient Load Balancing in Software Defined Networking

Using distributed SDN control, software defined networking (SDN) delivers additional flexibility to network management, and it has been a significant breakthrough in network innovation. Switch migration is often used for distributed controller workload balancing. The time-sharing switch migration (TSSM) scheme proposed a strategy in which multiple controllers are allowed to share the workload of a switch via time sharing during an overloaded condition, resulting in reduced ping-pong controller difficulty, fewer overload occurrences, and improved controller efficiency. However, it requires more than one controller to accomplish, it has greater migration costs and higher controller resource usage during the TSSM operating time. As a result, we presented a coalitional game strategy that optimizes controller selection throughout the TSSM phase depending on flow characteristics. The new TSSM method reduces migration costs and controller resource usage while still providing TSSM benefits. For the sake of practicality, the proposed strategy is implemented using an open network operating system. The experimental findings reveal that, as compared to the typical TSSM system, the proposed technique reduces migration costs and controller resource usage by approximately 18%.

МЕТОДИ ТА ЗАСОБИ ЗАХИСТУ РЕСУРСІВ В КОМП’ЮТЕРНІЙ SDN-МЕРЕЖІ

The work is dedicated to the analysis and improvement of methods and tools for building the architecture of software-defined networks (SDNs). A crucial aspect involves comparing the differences between network management using traditional methods and utilizing an SDN controller. Primary attention is given to the development of security models based on software-defined networks.The research is grounded in the analysis of specific cases involving the use of such networks, including gathering opinions and expert assessments from professionals in the field. It leverages publicly available information on methods and tools for securing the architecture of software-defined networks. SDN technology provides greater flexibility and speed in implementing security measures, allowing real-time responses to threats. In contemporary conditions, where cyber threats are becoming more complex and pronounced, SDN networks enable the detection of attacks, blocking malicious actions, and applying security policies in real-time. However, the increasing complexity of cyber threats and constant technological changes necessitate further development and enhancement of the information security of SDN network resources for companies. There is a need for further analysis and improvement of methods and tools for protecting information and other resources in computer-based SDN networks. The necessity for further analysis and improvement of methods and tools for protecting information and other resources in computer-based SDN networks serves as the foundation for this research. As a result, an enhancement in the level of information security for SDN network resources has been achieved by: 1) reducing the intervention time in the network; 2) applying an improved method of traffic processing based on network security of packet flows, allowing desired network applications to efficiently manage forwarding.

Performance Visualization of Southbound Interface in Software Defined Networking

Software Defined Networking (SDN) makes Internet network configuration easier by separating the control plane and data plane. The control plane on the controller has information on network devices in the data plane and centrally control these devices. One of the controllers in SDN being developed is the Open Network Operating System (ONOS). ONOS provides interfaces such as Representational State Transfer (REST) Application Programming Interface (API). The ONOS core REST API provides some information from the network connected to it, such as devices, statistics, and the information in JSON file. The primary objective of this study is to develop an interface that simplifies performance monitoring through graphical representation. This involves testing the visualization with various topologies and conducting a comparative analysis of the visualization results across these topologies. The creation of the interface entails presenting statistical data, available in the form of a JSON file from the ONOS controller via the REST API, on the web interface in graphical format. The resulting visualization generates a graph that aligns with the performance characteristics of each topology, reflecting device details, ports, and additional parameters such as the count of sent and received packets, as well as sent and received bytes. The performance visualization outcomes specific to each topology are consistent with the number of connections and are prominently displayed on the web interface. Additionally, this research evaluates network throughput and bandwidth by sending ICMP packet and iperf tests across each topology. Among all the openflow tests performed on various network topologies, it was observed that the tree topology exhibited the lowest network capacity utilization, followed by the leaf-spine topology, and finally the ring topology.

Named Multipath Depth-First Search: An SDN-based Routing Strategy for Efficient Failure Handling and Content Delivery in NDN

Information-centric networking (ICN) architectures, such as named data networking (NDN), have emerged as potential solutions for efficiently retrieving and delivering content. However, challenges remain regarding routing scalability, resilience, and caching efficiency. Software-defined networking (SDN) offers opportunities to optimize NDN implementations through centralized control and programmability. In this paper, we propose Named Multipath DFS, an SDN-based routing and caching scheme for NDN networks. NMDFS leverages a centralized controller to pre-compute multipath routes and implement coordinated caching. We evaluate NMDFS on an emulated topology testbed against default NDN and Named-data link state routing. The results demonstrate significant improvements with NMDFS, reducing overhead signalling costs by 94% and 78%, respectively, compared with other schemes. Round-trip latencies for content retrieval were reduced by up to 98%. The SDN controller’s global network view and control are leveraged to optimize content caching through packet loss-driven adaptation and eliminate redundant messaging, leading to substantial performance gains.

ML-based SDN performance prediction

Software-defined networking (SDN), a new type of network architecture with the advantages of programmability and centralized management, has become a promising solution for managing and optimizing network traffic in modern data centers. However, designing efficient SDN controllers and applications requires a deep understanding of their network performance characteristics. In this work, we implement a machine learning-based method for SDN performance prediction. Our method uses supervised learning to build a training model based on a set of publicly available real network traffic datasets and then uses the model to predict future network performance metrics, such as RTT, S2C, and C2C. Our method is evaluated in two different SDN distributed deployment structures, demonstrating its effectiveness in network performance prediction. We observed that XGBoost achieves the lowest error in most of the cases in terms of MAE, RMSE and MAPE, and feature selection through PCA fails to further improve the prediction performance of XGBoost.

P4toNFV: Offloading from P4 switches to NFV in programmable data planes

SummaryP4 combines the benefits of hardware‐based networking with the adaptability of software‐based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 s. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs.

Detection and Mitigation of DDOS Attack in SDN Environment Using Hybrid CNN-LSTM

DDoS attacks, powered by botnets to flood network resources, pose a significant threat to traditional network setups. Software-Defined Networking (SDN) boosts network adaptability and programmability by separating the control and data planes. However, the centralized control in SDN can be a vulnerability, allowing attackers to exploit security flaws and launch DDoS attacks. These attacks overwhelm network controllers and switches, consuming bandwidth and server resources, and disrupting regular user access. In response to the threat, we've implemented an online SDN defence system designed to detect and counter such attacks. This system includes modules for both spotting anomalies and handling them. The anomaly detection model combines Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) techniques to effectively spot irregular traffic patterns. For mitigation, the model identifies abnormal traffic by implementing flow rule orders from the controller and traces back to the attacker via IP tracing. To measure our approach's effectiveness, we used various evaluation metrics like Accuracy, F-measure, Precision, Recall, ROC Curve, and Precision-Recall Curve. Our methodology displayed impressive results, achieving a 99.83% accuracy in multiclass classification and 99.17% accuracy in binary classification. In comparison with existing DDoS detection systems, our AI-driven mitigation techniques demonstrated their superiority. Overall, our research aims to streamline the detection and mitigation of DDoS attacks.

A novel framework for capacitated SDN controller placement: Balancing latency and reliability with PSO algorithm

Software Defined Networking (SDN) is a new network architecture theoretical framework, which based on the partition of the control plane and the data plane. Some of the important features of SDN are centralized network management, open code, scalability, effective utilization of network resources, effective traffic management, security etc. All these administrative decisions taken by the controller which lies at the top of the data plane. Efficient placing of controllers is essential to enhance the network’s reliability, which ultimately improves the performance of the network. Much of the work done in the SDN has been aimed on controller placement problems (CPP) by considering latency and reliability of the network separately whereas few of them acknowledged both latency and reliability simultaneously. We considered both communication latency and reliable communication to solve CPP. To achieve this, we aim to minimize the total average latency of the network with a reliable network model that can work perfectly up to the failure of n-1 controllers out of n deployed. We first formulate an efficient algorithm “Capacitated Controllers Arrangement (CCA)” to define the problem and used nature-based metaheuristic technique, Particle Swarm optimization (PSO) to achieve the solution. By intelligently allocating controllers, this dynamic system minimizes network latency and optimizes switch assignments. Three controllers deliver excellent reliability and latency performance even despite controller breakdowns, according to experiments conducted on the Internet2 OS3E architecture. This novel method not only increases SDN efficiency but also establishes a standard for further improvements in controller placement tactics. The outcome shows that the proposed approach achieves its goal efficiently and in future other multi-objective optimization techniques will be explored to solve reliability aware CPP.