Software Defined Networking Applications Research Articles

A novel link fabrication attack detection method for low-latency SDN networks

The application of Software-defined Networking (SDN) in low-latency scenarios, such as 6G, has received immense attention. Notably, our research reveals that SDN remains susceptible to link fabrication attacks (LFA) in low-latency environments, where existing detection methods fail to effectively detect LFA. To address this issue, we propose a novel detection method called Correlated Link Verification (CLV). CLV is composed of three phases. Firstly, we introduce a data processing method to mitigate measurement error and enhance robustness. Secondly, we present a multipath transmission simulation method to convert the measured performance disparity between correlated links into statistical features. Thirdly, we propose a dynamic threshold calculation method, which utilizes the statistical features to determine thresholds based on extreme value theory and probability distribution fitting. Finally, CLV identifies the fabricated link within correlated links based on the thresholds and current statistical features. Extensive experiments have been conducted to validate the feasibility, effectiveness, scalability and robustness of CLV. The experimental results demonstrate that CLV can effectively detect LFA in low-latency SDN networks.

Addressing security challenges in industry 4.0: AVA-MA approach for strengthening SDN-IoT network security

In the context of Industry 4.0, the convergence of the Internet of Things (IoT) and Software Defined Networking (SDN) represents a challenging yet pivotal avenue for bolstering network security. This amalgamation facilitates the establishment of an integrated SDN-IoT platform that adeptly confronts the security intricacies arising from the evolving landscape of interconnected devices and industrial networks. Through the strategic application of SDN, the network architecture undergoes dynamic segmentation, responsive to diverse parameters encompassing device attributes, application contexts, and security imperatives. This segmentation strategy effectively isolates IoT devices, constricting the potential attack surface and correspondingly diminishing the ramifications of security breaches. Therefore, this paper proposed a novel Adaptive Variational Autoencoder-based Modified Archery (AVA-MA) method to enhance the security of IoT devices. In this study, samples are collected and used from both the TON-IoT dataset and the SDN dataset to determine the effectiveness of the proposed AVA-MA method. The evaluation measures such as AUC, F1-score, recall, accuracy, and precision are used to evaluate the performance of the proposed AVA-MA method. The results depict the AVA-MA method attained an accuracy of 98.95 %, precision of 97.54 %, recall of 95.64 %, and F1-score of 98.86 % respectively.

SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios

In the near future, there will be a greater emphasis on sharing network resources between roads and railways to improve transportation efficiency and reduce infrastructure costs. This could enable the development of global Cooperative Intelligent Transport Systems (C-ITSs). In this paper, a software-defined networking (SDN)-based common emergency service is developed and validated for a railway and road telecommunication shared infrastructure. Along with this, the developed application is capable of reducing the chances of distributed denial-of-service (DDoS) situations. A level-crossing scenario is considered to demonstrate the developed solution where railway tracks are perpendicular to the roads. Two cases are considered to validate and analyze the developed SDN application for common emergency scenarios. In case 1, no cross-communication is available between the road and railway domains. In this case, emergency message distribution is carried out by the assigned emergency servers with the help of the SDN controller. In case 2, nodes (cars and trains) are defined with two wireless interfaces, and one interface is reserved for emergency data communication. To add the DDoS resiliency to the developed system the messaging behavior of each node is observed and if an abnormality is detected, packets are dropped to avoid malicious activity.

Cognitive routing in software defined networks using learning models with latency and throughput constraints

<p>To address latency and throughput challenges in software defined networks (SDNs), the research investigates cognitive routing's revolutionary implications. In today's data-driven world, network performance optimisation is crucial. Cognitive routing is a dynamic and potentially disruptive network management technology. Cognitive routing, strengthened by reinforcement learning and adaptive decision-making, is crucial to network efficiency and responsiveness, according to our study. The results show that cognitive routing optimises performance by limiting delay and maximising throughput. SDN application cognitive routing engine (CRE) driving forces, design, and preliminary assessment are described in this article. The CRE finds almost optimal paths for a user's quality of service (QoS) need while minimising monitoring overhead. Instead of global monitoring to find optimal paths, local monitoring achieves this. In ad-hoc networks, finding a trustworthy path reduces latency and ensures network stability. The proposed system was simulated utilising many parameters. Compared to previous SDN-based systems, end-to-end latency and ping round-trip time were better.</p>

Hybrid Support Vector Machine for Predicting Accuracy of Conflict Flows in Software Defined Networks

Software Defined Networking (SDN) is an infrastructure platform for delivering simplified and compliant services with flexible services. These are the means of centralized maintenance and adaptive functions. SDN is affected by various contention flows and causes network performance issues. In this case, we need to provide efficient solutions to handle conflicting flows with better priority and actions. In this paper, we propose a DeepQ Residue method for analyzing normal and conflicting flow scenarios in the load balancing phase. During simulation, an open SDN network is generated using TensorFlow. We use a Hybrid Support Vector machine with an improved decision tree method to predict accuracy and performance. In this case, we analyze threads from 1000 to 100000 in increments of 10000 threads in each iteration. Here, we train a deep belief network with a decision-free feature for environmental simulation. Based on the simulation results, the accuracy of our proposed method reaches 97 %, and we compare the results with the results of various existing methods. Our proposed algorithm provides a high-performance SDN application with different conflicting load-balanced flows

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.

Packet Classification Using TCAM of Narrow Entries

Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

A Multi-Controller Placement Strategy for Hierarchical Management of Software-Defined Networking

Software-Defined Networking (SDN) is a new architecture with symmetric/asymmetric network structures that separates the control plane of network devices from the data plane, and a Controller Placement Problem (CPP) is a critical management problem in SDN. The main research content of the CPP is to determine the number and location of controllers placed in a network topology, as well as the connection relationship between controllers and switches. However, traditional CPP solutions based on symmetric/asymmetric structures may not be efficient to meet the increasing requirements of SDN applications. In order to improve the CPP solutions from the viewpoint of hierarchical management, this paper considers the CPP solutions as a multi-objective optimization problem based on symmetric/asymmetric structures in the SDN architecture. Thus, this paper then proposes a multi-controller placement strategy based on an improved Harris Hawks Optimization algorithm. Firstly, the local controller load is limited, and a Sin chaotic map is introduced to initialize the CPP scheme. The total latency of the network, the reliability of the node, the total failure rate of the link and the total placement cost are seriously considered when placing the controllers. Secondly, a Cos nonlinear function is added to the global search. A dynamic adaptive weight factor is used to smooth the switching approach between the global search and the local search, so as to enhance the global search ability. Then, a Cauchy variation perturbation is added to the obtained CPP scheme to strengthen the diversity of CPP schemes, and the CPP scheme with the Pareto front is finally solved. The topology simulation of three real large-scale SDN networks shows that the proposed strategy, based on an improved Harris Hawks Optimization algorithm, has more robust advantages in comparison to other algorithms.

The LOFT Attack: Overflowing SDN Flow Tables at a Low Rate

The emerging Software-Defined Networking (SDN) is being adopted by data centers and cloud service providers to enable flexible control. Meanwhile, the current SDN design brings new vulnerabilities. In this paper, we explore a stealthy attack that uses a minimum rate of attack packets to disrupt SDN data plane. To achieve this, we propose the LOFT attack that computes the lower bound of attack rate to overflow flow tables based on the inferred network configurations. Particularly, each attack packet always triggers or maintains consumption of one flow rule. LOFT can ensure the attack effect under various network configurations while reducing the possibility of being captured. We demonstrate its feasibility and effectiveness in a real SDN testbed consisting of commercial hardware switches. The experimental results show that LOFT incurs significant network performance degradation and potential network DoS at an attack rate of only tens of Kbps. To defeat the attack, we develop a data-to-control plane collaborative defense system named LOFTGuard, which is lightweight and transparent to SDN applications. Evaluations show that Guard effectively protects SDN against the attack and introduces a small overhead.

SSHS: SDN seamless handover system among LAN access points

SummaryIn recent years, artificial intelligence techniques, such as software‐defined networks (SDNs), machine learning classification (ML classification), and mobility models (MMs), have become vital in developing networks. Furthermore, communication methodologies, such as handover, directly affect network performance. In this paper, we propose a new system named SSHS, SDN Seamless Handover System, that combines SDN with an ML classifier to administer the network connection of mobile nodes. Through the SSHS system, the SDN will centralize the control to enable comprehensive management over the network, coupled with a decision tree (DT) classifier in the RYU controller to bring intelligence to the SDN application by enabling data analysis and prediction among mobile nodes generated by the RSSGM model. We present the SSHS model's effectiveness in providing a seamless communication handover among multiple access points (APs). The results of this study revealed that the SSHS provided a seamless handover among APs by improving the throughput by 26%, and decreasing the delay of arriving packets by 73% to standard SDN handover system.

TCAM-based packet classification for many-field rules of SDNs

Software-defined networking (SDN) provides an emerging paradigm for future network architectures. OpenFlow is a widely deployed south-bound protocol for SDN. It uses rule-based packet forwarding and each rule may support many header fields for fulfilling various SDN applications. As the number of inspected header fields increases, these long rules may complicate the procedure of packet classification based on TCAM because TCAM only supports limited word sizes. In this paper, we propose a TCAM-based packet classification scheme to support many-field rules. Our scheme incorporates multiple stages of TCAM accesses, where each stage only compares selected bits of rules. Although our scheme may result in additional TCAM accesses, it enables packet classification upon rules longer than TCAM words. The experimental results show that our scheme can search 10K 247-bit rules by using 72-bit TCAM entries, where each packet classification can be accomplished with less than ten TCAM accesses. It also reduces the required TCAM storage by a magnitude of order. In summary, with our scheme, both feasibility and scalability of TCAM-based packet classification for SDN rules are significantly improved.

SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning

Software-defined networks (SDNs) have the capabilities of controlling the efficient movement of data flows through a network to fulfill sufficient flow management and effective usage of network resources. Currently, most data center networks (DCNs) suffer from the exploitation of network resources by large packets (elephant flow) that enter the network at any time, which affects a particular flow (mice flow). Therefore, it is crucial to find a solution for identifying and finding an appropriate routing path in order to improve the network management system. This work proposes a SDN application to find the best path based on the type of flow using network performance metrics. These metrics are used to characterize and identify flows as elephant and mice by utilizing unsupervised machine learning (ML) and the thresholding method. A developed routing algorithm was proposed to select the path based on the type of flow. A validation test was performed by testing the proposed framework using different topologies of the DCN and comparing the performance of a SDN-Ryu controller with that of the proposed framework based on three factors: throughput, bandwidth, and data transfer rate. The results show that 70% of the time, the proposed framework has higher performance for different types of flows.

SDN Application Plane Where Intelligence Meets Networking

The software-defined networking (SDN) paradigm has revolutionized the way we think about network management and control. The SDN application plane is a critical component of this paradigm, where intelligent applications are leveraged to drive network behavior and optimize network performance. In this abstract, we explore the role of the SDN application plane in bridging the gap between traditional network management and the intelligent, dynamic network of the future. We discuss the challenges and opportunities of deploying SDN applications, including the need for standardized APIs and the importance of intelligent analytics and machine learning techniques. Finally, we highlight the transformative potential of the SDN application plane, from enabling new applications and services to improving network security and resilience. By bringing intelligence and networking together in new and innovative ways, the SDN application plane promises to revolutionize the way we design, manage, and secure our networks.

Coexistence of Railway and Road Services by Sharing Telecommunication Infrastructure Using SDN-Based Slicing: A Tutorial

This paper provides a detailed tutorial to develop a sandbox to emulate coexistence scenarios for road and railway services in terms of sharing telecommunication infrastructure using software-defined network (SDN) capabilities. This paper provides detailed instructions for the creation of network topology using Mininet–WiFi that can mimic real-life coexistence scenarios between railways and roads. The network elements are programmed and controlled by the ONOS SDN controller. The developed SDN application can differentiate the data traffic from railways and roads. Data traffic differentiation is carried out using a VLAN tagging mechanism. Further, it also provides comprehensive information about the different tools that are used to generate the data traffic that can emulate messaging, video streaming, and critical data transmission of railway and road domains. It also provides the steps to use SUMO to represent the selected coexistence scenarios in a graphical way.

A Comparative Study of Software Defined Networking Controllers Using Mininet

Software Defined Networking (SDN) is a relatively new networking architecture that has become the most widely discussed networking technology in recent years and the latest development in the field of developing digital networks, which aims to break down the traditional connection in the middle of the control surface and the infrastructure surface. The goal of this separation is to make resources more manageable, secure, and controllable. As a result, many controllers such as Beacon, Floodlight, Ryu, OpenDayLight (ODL), Open Network Operating System (ONOS), NOX, as well as Pox, have been developed. The selection of the finest-fit controller has evolved into an application-specific tool operation due to the large range of SDN applications and controllers. This paper discusses SDN, a new paradigm of networking in which the architecture transitions from a completely distributed form to a more centralized form and evaluates and contrasts the effects of various SDN controllers on SDN. This report examines some SDN controllers or the network’s “brains,” shows how they differ from one another, and compares them to see which is best overall. The presentation of SDN controllers such as Ryu, ODL, and others is compared by utilizing the Mininet simulation environment. In this study, we offer a variety of controllers before introducing the tools used in the paper: Mininet. Then, we run an experiment to show how to use ODL to establish a custom network topology on a Mininet. The experimental results show that the O controller, with its larger bandwidth and reduced latency, outperforms other controllers in all topologies (both the default topology and a custom topology with ODL).

A Dynamic Handover Software-Defined Transmission Control Scheme in Space-Air-Ground Integrated Networks

The Space-Air-Ground Integrated Networks (SAGINs) converges the rapidly developing ground, aerial and satellite communication networks to provide users with efficient and personalized services through the multi-layer network architecture. The complex structure, inconsistent communication protocol, and incompatible equipment significantly affect the information transmission efficiency in SAGINs. The application of Software Defined Network (SDN) technology promotes global deployment of the SAGIN resources. Aiming at the information transmission bottleneck between controller and switch nodes in the software-defined SAGINs, a dynamic handover transmission control scheme based on the queuing game model is proposed. Considering long transmission delay links in the SAGINs, the traditional queuing game model is improved to reduce the number of information interactions. The users’ service value are defined as the random distribution to describe the various requirements in the network. In addition, the relationship between the social welfare and the arrival rate of the transmission control system is discussed under two modes of observable and unobservable controller queues. Through theoretical analysis and calculation, the unique handover arrival rate is obtained to make the social welfare equal in the two modes. When the arrival rate is less than the handover threshold, the controller queue is set to unobservable mode, and otherwise to observable mode. Then, the system can operate in an optimal way to obtain the maximum benefit. At last, numerical and system simulation verify the effectiveness of the scheme.

Early Detection of Abnormal Attacks in Software-Defined Networking Using Machine Learning Approaches

Recent developments have made software-defined networking (SDN) a popular technology for solving the inherent problems of conventional distributed networks. The key benefit of SDN is the decoupling between the control plane and the data plane, which makes the network more flexible and easier to manage. SDN is a new generation network architecture; however, its configuration settings are centralized, making it vulnerable to hackers. Our study investigated the feasibility of applying artificial intelligence technology to detect abnormal attacks in an SDN environment based on the current unit network architecture; therefore, the concept of symmetry includes the sustainability of SDN applications and robust performance of machine learning (ML) models in the case of various malicious attacks. In this study, we focus on the early detection of abnormal attacks in an SDN environment. On detection of malicious traffic in SDN topology, the AI module in the topology is applied to detect and act against the attack source through machine learning algorithms, making the network architecture more flexible. Under multiple abnormal attacks, we propose a hierarchical multi-class (HMC) architecture to effectively address the imbalanced dataset problem and improve the performance of minority classes. The experimental results show that the decision tree, random forest, bagging, AdaBoost, and deep learning models exhibit the best performance for distributed denial-of-service (DDoS) attacks. In addition, for the imbalanced dataset problem of multiclass classification, our proposed HMC architecture performs better than previous single classifiers. We also simulated the SDN topology and scenario verification. In summary, we concatenated the AI module to enhance the security and effectiveness of SDN networks in a practical manner.

Centralized and Distributed Intrusion Detection for Resource-Constrained Wireless SDN Networks

Software-defined networking (SDN) was devised to simplify network management and automate infrastructure sharing in wired networks. These benefits motivated the application of SDN in resource-constrained wireless networks to leverage solutions for complex applications. However, some of the core SDN traits expose the networks to Denial-of-Service (DoS) attacks. There are proposals in the literature to detect DoS in wireless SDN networks; however, not without shortcomings: there is little focus on resource constraints, high detection rates have been reported mostly for small networks and the detection is disengaged from the identification of the type of attack or the attacker. Our work targets these shortcomings by introducing a lightweight, online change point detector to monitor performance metrics that are impacted when the network is under attack. A key novelty is that the proposed detector is able to operate in either centralized or distributed mode. The centralized detector has very high detection rates and can further distinguish the type of attack from a list of known attacks. In turn, the distributed detector can be useful to identify the nodes launching the attack. Our proposal is tested over IEEE 802.15.4 networks. The results show detection rates exceeding 96% in networks of 36 and 100 nodes and identification of the type of attack with a probability exceeding 89% when using the centralized approach.

Application of Software-Defined Networking

Software-Defined Networking (SDN) is an architecture purporting to be dynamic, manageable cost-effective, and adaptable, seeking to be suitable for the high bandwidth, dynamic nature of today’s applications. SDN architectures decouple network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstract from application and network services. In this paper, the advantages and challenges of Software-Defined Networking (SDN) are discussed.However, the Software Defined Networking application (SDN App) and the application of SDN are also discussed in detail.

Generic Method for SDN Controller Selection Using AHP and TOPSIS Methods

The control plane plays an essential role in the implementation of Software Defined Network (SDN) architecture. Basically, the control plane is an isolated process and operates on control layer. The control layer encompasses controllers which provide a global view of the entire SDN. The Controller selection is more crucial for the network administrator to meet the specific use case. This research work mainly focuses on obtaining a better SDN controller. Initially, the SDN controllers are selected using integrated Analytic Hierarchy Process and Technique for Order Preference Similarity to Ideal Solution (AHP and TOPSIS) method. It facilitates to select minimal number of controllers based on their features in the SDN application. Finally, the performance evaluation is carried out using the CBENCH tool considering the best four ranked controllers obtained from the previous step. In addition, it is validated with the real-time internet topology such as Abilene and ERNET considering the delay factor. The result shows that the “Floodlight” controller responds better for latency and throughput. The selection of an optimum controller-Floodlight, using the real-world Internet topologies, outperforms in obtaining the path with a 28.57% decrease in delay in Abilene and 16.94% in ERNET. The proposed work can be applied in high traffic SDN applications.