Centralized Software Defined Networking Controller Research Articles

EN-LAKP: Lightweight Authentication and Key Agreement Protocol for Emerging Networks

Emerging network technologies that will be developed and implemented in the next generation to enhance computing facilities are Wireless Sensor Networks (WSNs) and Software Defined Networking (SDN). Since micro-electromechanical system (MEMS) technology progress, WSNs become more and more popular since they offer realistic and affordable real- time monitoring options. As a result, as network traffic and the variety of sensor nodes rise, the SDN paradigm is being integrated into WSN to avoid a performance bottleneck with traditional network design. Since the sensor nodes are dispersed throughout a hostile environment and they communicate over a public channel, the information shared between entities is vulnerable to attack. The proposed protocol illustrated why it made sense to provide network administration and control responsibilities to centralized SDN controller nodes. We thus offer the Lightweight Authentication and Key Agreement Protocol (LAKP) for SDN-enabled WSNs to protect entity communication. Furthermore, we demonstrate that the suggested solution avoids known security vulnerabilities by doing both formal and informal security examinations using the Scyther tool and Burrows-Abadi-Needham (BAN) logic.

A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments

Software Defined Networking (SDN) threats make network components vulnerable to cyber-attacks, creating obstacles for new model development that necessitate innovative security countermeasures, like Intrusion Detection Systems (IDSs). The centralized SDN controller, which has global view and control over the whole network and the availability of processing and storing capabilities, makes the deployment of artificial intelligence-based IDS in controllers a hot topic in the research community to resolve security issues. In order to develop effective AI-based IDSs in an SDN environment, there must be a high-quality dataset for training the model to offer effective and accurate attack prediction. There are some intrusion detection datasets used by researchers, but those datasets are either outdated or incompatible with the SDN environment. In this survey, an overview of the published work was conducted using the InSDN dataset from 2020 to 2023. Also, research challenges and future work for further research on IDS issues when deployed in an SDN environment are discussed, particularly when employing machine learning and deep learning models. Moreover, possible solutions for each issue are provided to help the researchers carry out and develop new methods of secure SDN.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

Dynamic SDN Controller Placement based on Deep Reinforcement Learning

Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

EN-LAKP: Lightweight Authentication and Key Agreement Protocol for Emerging Networks

In the next generation, emerging network technologies like Software Defined Networking (SDN) and Wireless Sensor Networks (WSNs) will be developed and deployed to improve computing facilities. Micro-electromechanical system technologies advance, and WSNs gain in popularity because they provide real-time monitoring solutions that are both economically and practically viable. The SDN paradigm is consequently being incorporated into WSN to prevent a performance bottleneck with traditional network architecture as network traffic and diverse sensor nodes increase. Because they interact through a public channel and the sensor nodes are spread throughout a hostile environment, the information transmitted among entities is subject to assault. The proposed protocol showed how centralized SDN controller nodes logically assumed the role of network management and control. Therefore, we present a lightweight authentication and key agreement mechanism for SDN-enabled wireless sensor networks to protect entity communication. Additionally, we demonstrate that the suggested system prevents known security flaws by conducting both informal and formal security studies using the Scyther tool and BAN logic. Further, the performance study demonstrates that the suggested scheme performs better in computing and communication burdens than related protocols with 1.6% to 5.4% and 1.3% to 5.8%, respectively.

Creation of the method of multipath routing using known paths in software-defined networks

The object of the research is the creation of a multipath routing algorithm for software-defined networks (SDN) including known paths, subject of the research is network parameters, designed according to a certain topology and using the developed algorithm. One of the most problematic aspects of contemporary computer networks (including applied SDN networks) is overloading. This results in harder control and limiting traffic and amount of users. Most routing algorithms that are used today have a rather large time complexity. In the course of the study, the following methods were used: study of known routing solutions for SDN networks and results of their application, method of path metric computation on the given topology by the amount of «hops» (transitions between network nodes), optimization of procedure for finding the path using SDN technology capabilities. These methods were united and integrated into the development of the overall routing algorithm, which is proposed in this article. The proposed multipath routing algorithm allows for the improvement of the process of traffic construction in the SDN network. This was achieved by decreasing the time complexity of the routing algorithm through the usage of previously known paths in the topology without the need to construct new ones. Involvement in the modification of the algorithm of forming distance vectors facilitated timely network reconfiguration in case its state changed. Using a centralized SDN controller made it possible to increase the stability of the network and save all configuration data in one place. The above factors make it possible to deploy an SDN network on an Edge architecture. Obtained results of the application of the multipath routing algorithm allow to consider it effective when compared with previously proposed algorithms, based on obtained results from a practical network model, where the proposed multipath routing algorithm is used. This is because the research task was formed correctly, and the solution for this task gave correct results. Results of using the described algorithm are demonstrated, and an analysis of the obtained results is conducted, which makes it possible to confirm the accuracy of scientific research.

Design and Implementation of Enhanced Programmable Data Plane Supporting ICN Mobility

Information-centric networking (ICN) separates the identifier and locator of network entities, providing a natural advantage in supporting mobility. To gain the advantage of ICN to support mobility, an urgent challenge is the problem of practical implementation with performance optimization. Software-defined networking (SDN) can be regarded as infrastructure to implement ICN mobility. However, it is difficult for the centralized SDN controller to quickly process mobile signaling. Therefore, this paper proposes enhanced programmable data plane supporting ICN mobility. By offloading mobility-related control plane functions from the controller to the data plane, the data plane can locally process mobile signaling without interacting with the controller. We propose an offloading mechanism for control plane functions, based on a rule table, where the controller authorizes the data plane to process the mobile signaling by loading the programmable rule table to data plane’s control element, and the control element intercepts the mobile signaling, matches the predefined rule table, and executes a series of application logic actions. In addition, we propose an improved SmartSplit algorithm to manage the rule table and speed up packets matching the rule table. Based on Intel’s Data Plane Development Kit (DPDK), we implement the enhanced programmable data plane. Our experimental results prove that the proposed enhanced programmable data plane has a stronger ability to process mobile signaling and reduce latency.

Drone-based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics

Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics.

Software-Defined Vehicular Networks With Trust Management: A Deep Reinforcement Learning Approach

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ETX$ </tex-math></inline-formula> ) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

Auction Design for Edge Computation Offloading in SDN-Based Ultra Dense Networks

Relying on offloading computation tasks to the network edge, ultra dense networks (UDNs) are capable of providing delay-aware service to nearby users. Meanwhile, software defined networking (SDN) is deemed as an effective technology to ease the management of infrastructure plane and control plane in UDNs, which is termed as SDN-based ultra dense networks. Specifically, the centralized SDN controller is capable of managing the whole network globally. With the increasing demands for various applications as well as the limitation of computation, storage and communication resource, how to allocate spectrum resource appropriately is imperative. In this article, we mainly show solicitude for spectrum sharing and edge computation offloading problems in SDN-based ultra dense networks, constituted of various macro base stations (MBSs), small-cell base stations (SBSs) and user equipments (UEs). To address this issue, we propose a second-price auction scheme for ensuring the fair bidding for spectrum rent, which enables the MBS edge cloud and SBS edge cloud to occupy the channel in cooperative and competitive modes. Moreover, the MBS edge cloud is termed as the buyer, and the SBS edge clouds are the sellers who sell the offloading resource to the MBS edge cloud. To be specific, the spectrum sharing and computation offloading scheme is executed in the SDN controller, and the controller is responsible for distributing spectrum allocation instructions to the infrastructure plane. Finally, experimental results validate the effectiveness of our proposed scheme in SDN-based ultra dense networks.

An Indirect Controller-Legacy Switch Forwarding Scheme for Link Discovery in Hybrid SDN

Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%-97.9% in the number of ports detected and reduction in number of messages of about 25%-58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

Link Discovery Attacks in Software-Defined Networks: Topology Poisoning and Impact Analysis

Software Defined Networking (SDN) has become a popular technology that offers advantages of programmable and flexible network management over the legacy practice. The centralized SDN controller is an important enabler of these benefits. One of the most crucial tasks of the SDN controller is link discovery as it provides topology of the network essential for the controller to direct or create rule forwarding and routing mechanisms. Much research on SDN security has been studied but only recently that security of OpenFlow link discovery protocols and topology poisoning have been addressed. Existing work includes link fabrication attacks via compromised hosts and defense systems with authentication. This paper discusses SDN link discovery process and its vulnerability to link discovery attacks including new attacks via compromised switches. We present a simple but effective defense mechanism using active ports that can detect both host-based and switch-based link discovery attacks. Finally, the paper presents an analytical and empirical analysis of the impacts of topology attacks on routing. The paper discusses attack details, proposed methods and results of these analyses.

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

Flow routing can achieve fine-grained network performance optimizations by routing distinct packet traffic flows over different network paths. While the centralized control of Software-Defined Networking (SDN) provides a control framework for implementing centralized network optimizations, e.g., optimized flow routing, the implementation of flow routing that is adaptive to varying traffic loads requires complex models. The goal of this study is to pursue a model-free approach that is based on reinforcement learning. We design and evaluate QR-SDN, a classical tabular reinforcement learning approach that directly represents the routing paths of individual flows in its state-action space. Due to the direct representation of flow routes in the QR-SDN state-action space, QR-SDN is the first reinforcement learning SDN routing approach to enable multiple routing paths between a given source (ingress) switch-destination (egress) switch pair while preserving the flow integrity. That is, in QR-SDN, packets of a given flow take the same routing path, while different flows with the same source-destination switch pair may take different routes (in contrast, the recent DRL-TE approach splits a given flow on a per-packet basis incurring high complexity and out-of-order packets). We implemented QR-SDN in a Software-Defined Network (SDN) emulation testbed. Our evaluations demonstrate that the flow-preserving multi-path routing of QR-SDN achieves substantially lower flow latencies than prior routing approaches that determine only a single source-destination route. A limitation of QR-SDN is that the state-action space grows exponentially with the number of network nodes. Addressing the scalability of direct flow routing, e.g., through routing only high-rate flows, is an important direction for future research. The QR-SDN code is made publicly available to support this future research.

A Multipath Transport Scheme for Real-Time Multimedia Services Based on Software-Defined Networking and Segment Routing

It is still a challenging task to provide satisfactory quality of experience (QoE) to users by traditional routing mechanism in current public Internet for real-time multimedia services with high bandwidth and strict end-to-end latency constraint. Multipath transmission is a promising way to improve the performance of data delivery, due to its advantage in bandwidth aggregation capacity over multiple paths to speed up high volume transfers between endpoints. However, existing multipath transmission has many disadvantages such as the requirements for end-host support (i.e. the necessary multi-homed network setting, and operating system or upper applications updates of user devices), and blindingly pursuing maximum of transmission throughput. Path allocation and end-to-end transmission control are two important factors affecting the quality of multipath transmission. As an emerging network architecture, software-defined networking (SDN) is flexible, manageable, and responsive to rapid changes in traffic requirements. In this paper, we propose a multipath transport scheme based on SDN with segment routing (SR) which can meet the bandwidth and low end-to-end transmission latency requirements of real-time interactive multimedia services. The centralized SDN controller is extended to allocate multiple disjoint paths meeting bandwidth requirement meanwhile balancing the network load. The proprietary multipath media server (MMS) dynamically controls the assignment between subflows and routing paths to reduce the end-to-end transmission latency and thus improve QoE of end-users. To show the feasibility of our approach, we construct an SDN platform using Mininet. Simulation results show that our approach performs better compared to the conventional approaches in terms of traffic balancing and end-to-end transmission delay.

SDN-Enabled Network Coding-Based Secure Data Dissemination in VANET Environment

Security and reliability in data transmission are considered as the challenging concerns in VANET. Software-defined networking (SDN) and network coding (NC) are the two key apprehensions in networking that have caught much attention from both industry and academia in recent years. Decoupling of control plane and data plane in SDN enables the centralized control of the network, providing flexibility to a great extent. On the other hand, NC has shown immense potential for improving robustness and security when deployed on VANET. This article advocates for the design of an architecture which exercises the SDN concept by incorporating NC with multigeneration-mixing (MGM) functionalities to increase reliability as well as security of data transmission in vehicular networks. We design an MGM-based NC protocol for encoding and decoding of data. Moreover, a centralized SDN controller takes charge of authentication of vehicles. Finally, we build a simulation model based on realistic traffic and communication characteristics. The simulation results show that the proposed protocol leveraged by the SDN framework outperforms the conventional NC-based protocol in case of security and reliability. Moreover, the simulation model is exhibited and point-by-point execution assessment is given to demonstrate the scalability and prevalence of the proposed strategy.

Evaluation and comparison of OpenDayLight and open networking operating system in software-defined networking

Software-defined networking (SDN) is a new way of transforming networks. It works on the principle of separating the infrastructure plane from the control plane. In the control plane, the centralized SDN controller acts as a brain of the network. Selecting the best SDN controller for the network is one of the most crucial tasks. In this paper, the experimental comparison between the two most powerful and popularly known SDN controllers on which many other SDN controllers are laid down their foundations i.e. OpenDayLight (ODL) and open networking operating system (ONOS) is performed. The experimentation is carried out on the Mininet emulation tool. Real-time packets are captured and analyzed using Wireshark. The results of the comparison concluded that the performance of the ODL controller is better than the ONOS in terms of burst rate, throughput, Round Trip Time (RTT) and bandwidth.

Reducing the effects of DoS attacks in software defined networks using parallel flow installation

Software defined networking (SDN) is becoming more and more popular due to its key features, such as monitoring, fine-grained control, flexibility and scalability. The centralized control of SDN makes it vulnerable to various types of attacks, e.g., flooding, spoofing, and denial of service (DoS). Among these attacks, DoS attack has the most severe impact because it degrades the performance of the SDN by overloading its different components, i.e., controller, switch, and control channel. This impact becomes more prominent in SDNs having fine-grained control over traffic for monitoring and management purposes, where large numbers of flow rules are installed. Existing approaches handle DoS attacks in SDN either by dropping malicious packets or by aggregating flow rules, resulting in a legitimate packet drop or loss of fine-grained control over network traffic. In this paper, a parallel flow installation approach is proposed to reduce the effects of DoS attacks, without losing the monitoring capability and fine-grained control over network traffic. The proposed approach installs flow rules in all switches along the path from the source to the destination on a single request from the source; resulting in a considerable reduction of control channel traffic and controller’s utilization. The proposed approach is evaluated by comparing it with the basic SDN controller. The simulation results show that the proposed approach increases the SDN performance in terms of CPU utilization, response time, flow requests, and control channel bandwidth.

Cross Layer Optimization of Wireless Control Links in the Software-Defined LEO Satellite Network

The low earth orbit (LEO) satellite network can benefit from software-defined networking (SDN) by lightening forwarding devices and improving service diversity. In order to apply SDN into the network, however, reliable SDN control links should be associated from satellite gateways to satellites, with the wireless and mobile properties of the network taken into account. Since these characteristics affect both control link association and gateway power allocation, we define a new cross layer SDN control link problem. To the best of our knowledge, this is the first attempt to explore the cross layer control link problem for the software-defined satellite network. A logically centralized SDN control framework constrained by maximum total power is introduced to enhance gateway power efficiency for control link setup. Based on the power control analysis of the problem, a power-efficient control link algorithm is developed, which establishes low latency control links with reduced power consumption. Along with the sensitivity analysis of the proposed control link algorithm, numerical results demonstrate low latency and high reliability of control links established by the algorithm, ultimately suggesting the feasibility, both technical and economical, of the software-defined LEO satellite network.

Flexible SDN control in tactical ad hoc networks

Modern tactical operations have complex communication and computing requirements that cannot be supported by today’s mobile ad hoc networks. The emerging Software Defined Networking (SDN) paradigm has the potential to enable the redesign and successful deployment of these systems. An SDN-based approach, however, will also bring new challenges since the SDN architecture was not designed to accommodate the requirements of an ad hoc network environment. Unreliability and dynamism may fragment the tactical network making the centralized SDN controller unsafe. To address these issues, in this paper, we propose flexible protocols that split the control of the ad hoc network between the centralized SDN controller and the data plane nodes. The latter can dynamically decide whether to follow the controller instructions or adapt to network changes in a distributed manner. We implement a proof-of-concept prototype of a flexible SDN ad hoc system and perform experiments to measure its performance benefits over traditional OpenFlow. Going a step further, we study theoretical, yet practical, methods of managing the overheads of flexible control which are crucial for the successful large-scale deployment of these systems. Evaluations on a real tactical ad hoc network dataset demonstrate that up to 50% more packets can be routed to their destinations if flexible SDN control is enabled, while the extra overheads are one order of magnitude lower than the control message overheads of traditional OpenFlow systems.

Compressed Sensing-Based Cached Content Locating for ICN

Locating the cached content accurately and timely is critical for utilizing in-network storage in information-centric networks (ICN). However, an enormous number of cached contents and their volatility nature make it a challenge to collect and maintain a consistent view of the network cache information. In this letter, we propose a novel cached content locating mechanism for ICN, which jointly uses the bloom filter and compressed sensing to efficiently represent the cached contents and leverages the ability of centralized control of software-defined networking (SDN) to make the cache information consistent in the SDN controller. Simulation results show a large saving in announcement cost with a tolerable decrease in locating accuracy.