Software Defined Networking Network Research Articles

An ensemble-based approach for effective distributed denial of service attack detection in software defined networking

<p>Software defined networking (SDN) is a network framework that aims to redefine network characteristics through the programmability of network components, faster and larger network monitoring, centralized network operation, and effective detection of fraudulent traffic and special malfunctions. However, SDN networks are vulnerable to security threats that can cause complete network failure. To address this issue, in this paper, machine learning techniques are suggested for the swift detection of attacks. Various methods for detecting distributed denial of service (DDoS) attacks are evaluated, and the study identifies the most precise method for categorizing such attacks within a SDN network. The results indicate that the proposed system achieves high accuracy in detecting DDoS attacks, with ensemble learning achieving 99% accuracy. This indicates a remarkable improvement percentage in comparison to the approaches of decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM).</p>

A model transformation based security policy automatic management framework for software-defined networking

Software-defined networking (SDN) has been utilized to enforce the security of traditional networks. However, the existing SDN-based security enforcement mechanisms rely heavily on the security policies containing the underlying information of data plane, such as MAC address, IP address or switch ports. These security policies need to be specifically developed by network operators and loaded into the control plane manually. With increasing the scale of underlying network, the existing security policy management mechanisms confront more and more challenges. The security policy transformation for SDN networks is to research how to transform the high-level security policy without containing the underlying information into the practical flow entries used by Openflow switches automatically, thereby implementing the automatic management of security policies. To achieve this objective, we propose a model transformation based security policy automatic management framework for software-defined networking in this paper. Leveraging its functional modules, the framework can solve the problems of how to find a connected path for each access control rule of security policy model (SPM) in data plane, how to transform the connected path into the system model of flow entries, as well as how to generate the practical flow entries according to the system model of flow entries. In order to validate the effectiveness and performance of framework, we implement the framework by leveraging POX controller and Mininet emulator. The experimental results illustrate the framework can transform SPM into practical flow entries, synchronously perceive the modifications caused by cutting down one connected path or changing SPM, and continuously keep the data plane holding the security properties defined by SPM at runtime.

BCSDNCC: A Secure Blockchain SDN framework for IoT and Cloud Computing

Rapid progress can be observed in the field of computer network technologies. Blockchain technology(BCT) presents a potentially viable alternative for effectively mitigating performance and security issues encountered in distributed systems. Recent studies have focused on exploring a number of exciting new technologies, including BlockChain (BC), Software-Defined Networking (SDN), and the Internet of Things (IoT). Various technologies offer data integrity and secrecy. One such technology that has been utilized for a number of years is cloud computing (CC). Cloud architecture facilitates the flow of confidential information, enabling customers to access remote resources. CC is also accompanied with notable security dangers, concerns, and challenges. In order to tackle these difficulties, we suggest integrating BC and SDN into a CC framework designed for the IoT. The fundamental flexibility and centralized capabilities of SDN facilitate network management, facilitate network abstraction, simplify network evolution, and possess the capacity to effectively handle the IoT network. The utilization of BCT is widely acknowledged as a means to ensure robust security inside distributed SDN (DSDN) and IoT networks, hence enhancing the efficacy of the detection and mitigation procedures.

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

A review of SDN‐enabled routing protocols for Named Data Networking

AbstractNamed Data Networking is a novel network architecture for the future internet in which data is named rather than addressed. This enables routers to forward data more efficiently based on its name, outperforming IP‐based routing. Another networking architecture that divides the control plane and the data plane is software‐defined networking (SDN). In an SDN network, the control plane decides on routing while the data plane forwards data packets. This separation increases routing flexibility and scalability. Numerous techniques to improve routing can be achieved with NDN and SDN integration. This paper provides an in‐depth examination of routing approaches in NDN based on SDN, emphasizing design principles, algorithms, and performance measures. We begin by summarizing the NDN architecture and delving into its essential components. We next go into the core routing ideas in NDN and categorize and study several routing solutions based on Software Defined Networks. Finally, we highlight the need for scalable, effective, and secure routing systems that may satisfy the expanding requirements of the contemporary internet. We also suggest open research topics in NDN routing based on SDN. This review provides an extensive overview of current centralized routing approaches in NDN, including their limitations and future possibilities.

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

Application of SDN-IP hybrid network multicast architecture in Commercial Aerospace Data Center

Abstract The increasing amount of massive data generated by commercial spacecraft in orbit puts forward higher and higher requirements for the stability, reliability, and computing power of computer systems for commercial aerospace data center. Data center computer systems are gradually transforming from X86 architecture and IP network model to a platform model with cloud computing and software-defined network (SDN) technology. This article proposes a new network architecture based on a unicast/multicast protocol for data interaction between the SDN and IP network. There are three main contributions of this article. The first is that the architecture proposed in this article aims to reduce end-to-end transmission latency and packet loss. The second is to improve the flexibility of system configuration and precise control when the SND controller state changes. The third is to verify the feasibility of deploying network architecture in a real data center environment.

Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation

Software Defined Networking (SDN) has revolutionized network administration by providing centralized management through software, enabling traffic adjustment independent of the data plane. Despite the benefits, SDN networks are prone to security threats from external sources, thus necessitating the implementation of security measures. Unfortunately, most existing efforts have been just a simple mapping of earlier solutions into the SDN environments. This paper addresses the problem of SDN security based on deep learning in a purely native SDN environment, where a Deep Learning intrusion detection module is tailored to a native SDN environment. In particular, we propose a hybrid Deep AutoEncoder with a Random Forest classifier model (DAERF) to enhance intrusion detection performance in a native SDN environment. The proposed model is incorporated into a novel adaptive framework for attack mitigation in SDN environments. The proposed framework consists of a three-layer protection mechanism for detecting and preventing attacks. It is based on entropy-based detection, hybrid machine learning in the control layer and proactive services monitoring in the application layer. Experimental results have shown that our DEARF proposed autoencoder model achieved anomaly detection rates in excess of 98% in stand-alone mode as well as when incorporated within the framework, making it highly solution for next generation SDN networks.

Comparative Analysis of Cloud based SDN and NFV in 5g Networks

The acceleration of 5G and other communication technologies is significantly influenced by digitization. However, the current state of 5G development falls short in meeting various performance and manageability requirements, particularly in supporting data center networks. To overcome these challenges, telecom enterprises commonly utilize Network Function Virtualization (NFV) and Software-Defined Networking (SDN) in their data centers. This paper introduces a comprehensive SDN-NFV architecture aimed at simplifying network administration tasks for telecom enterprises. The evolution of interconnect network operations for virtual services has undergone substantial transformations due to the adoption of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). We conducted an investigation into the SDN/NFV paradigm to determine the effective implementation, management, and distribution of network services to end users. This compilation of research papers delves into various aspects of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) within the realm of emerging technologies. Aissioui et al. (2015) contribute with an elastic distributed SDN/NFV controller designed for the efficient management of 5G mobile cloud systems, addressing their dynamic nature. Alenezi et al. (2019) focus on cloud-based SDN and NFV architectures tailored for IoT infrastructure, emphasizing scalability and flexibility. Barakabitze et al. (2020) provide an extensive survey on 5G network slicing using SDN and NFV, categorizing approaches and discussing future challenges.

HR‐DO: High reliable–delay optimized controller placement in software‐defined networking

SummaryCurrently, networking technologies are pivoting towards software‐defined networking (SDN) due to its easy management and centralized control system. The SDN network metrics, such as latency, scalability, robustness, and reliability largely depends on controller placement in the network domain. In view of betterment and best progression of network quality, the above mentioned network metrics need to be laid more emphases by considering controller placement. To achieve better network quality, an HR‐DO (high reliable–delay optimized controller placement) is proposed. Fuzzy C‐means, a meta‐heuristic optimization algorithm, is used to optimize the number of controllers required in the network due to its effectiveness in cluster selection. It also eases the network latency. In addition, a minimal cut set‐based Boolean logic approach and natural connectivity metric are used to improve the reliability and robustness of SDN with minimum worst‐case latency. To check the performance, the proposed HR‐DO is implemented on a topology‐zoo network data set (OS3E and Chinanet). For comparison purposes, OCPA and K‐means algorithms are taken into the account of latency, reliability, and robustness as parameters. The performance of the HR‐DO approach outperforms over K‐means and OCPA with respect to latency, reliability, and robustness.

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.

Flow-based Service Time optimization in software-defined networks using Deep Reinforcement Learning

A Ternary Content-Addressable Memory (TCAM) constitutes a memory variant employed within Software Defined Networking (SDN) node flow tables. These TCAMs deliver swift processing, enabling rapid parallel lookups. Nonetheless, due to their high energy consumption and cost, TCAMs have limited dimensions. This size constraint influences rule capacity, and suboptimal rule management can degrade network quality of service. Although different techniques for flow table management have been proposed during recent years, such as eviction, idle and hard timeout mechanisms, this paper proposes a Deep-Reinforcement Learning (DRL) solution, namely DRL-Idle, that is able to maximize the service time of the flows in an SDN network without considering any assumption about their status throughout time. By means of a continuous learning, DRL-Idle is able to also minimize the number of rule installations that are required to serve the target flows. Based on the idea of dynamically modifying the idle timeout value of the flows according to their needs, DRL-Idle outperforms existing approaches aiming at solving the Rule Placement Problem, averaging a 30% increase in performance.

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.

Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response

Software-defined networking (SDN) is an innovative technology that has the potential to enhance the scalability, flexibility, and security of telecommunications networks. The emergence and development of SDNs have introduced new opportunities and challenges in the telecommunications industry. One of the major challenges encountered by SDNs is the timing side-channel attacks. These attacks exploit timing information to expose sensitive data, including flow tables, routes, controller types, and ports, which pose a significant threat to communication networks. Existing techniques for mitigating timing side-channel attacks primarily focus on limiting them via network architectural changes. This significantly increases the overhead of SDNs and makes it difficult to identify the origin of the attack. To secure resilient integration of SDN in telecommunications networks, it is necessary to conduct comprehensive research that not only identifies the attack activity, but also formulates an adequate response. In this paper, we propose a detection and response solution for timing side-channel attacks in SDN. We used a machine learning-based approach to detect the probing activity and identify the source. To address the identified timing side-channel attack queries, we propose a response mechanism. This entails devising a feedback-oriented response to counter the identified source, such as blocking or diverting it, while minimising any adverse effects on legitimate network traffic. This methodology is characterised by an automated data-driven approach that enables prompt and effective responses. The architecture of this security solution ensures that it has a minimal impact on network traffic and resource usage as it is designed to be used in conjunction with SDN. The overall design findings show that our detection approach is 94% precise in identifying timing side-channel attacks in SDN when compared with traditional mitigation strategies. Additionally, the response mechanism employed by this approach yielded highly customised and precise responses, resulting in an impressive accuracy score of 97.6%.

Maximizing SDN resilience to node‐targeted attacks through joint optimization of the primary and backup controllers placements

AbstractIn software defined networks (SDN) packet data switches are configured by a limited number of SDN controllers, which respond to queries for packet forwarding decisions from the switches. To enable optimal control of switches in real time the placement of controllers at network nodes must guarantee that the controller‐to‐controller and switch‐to‐controller communications delays are bounded. Apart from the primary controllers that control the switches in the nominal state, separate backup controllers can be introduced that take over when the primary controllers are unavailable, and whose delay bounds are relaxed. In this paper, we present optimization models to jointly optimize the placement of primary and backup controllers in long‐distance SDN networks, aimed at maximizing the network's resilience to node‐targeted attacks. Applying the models to two well‐known network topologies and running a broad numerical study we show that, when compared with the standard approach of using only primary controllers, the use of backup controllers provides significant resilience gains, in particular in case of tight delay bounds.

Shortest Path Routing Performance Evaluation over SDN Environment

Static routing has a manual configuration setup system, and the scope of static routing in an SDN network is just for small networks. The solution to this problem rises up with the new technology defined as software-defined networking (SDN) based on shortest path first dynamic routing. SDN has the facility of a centralized controller that smooth the controls and routes computation over a data packet. The performance analysis of SDN networks that have SDN switches connected to the network based on the shortest path first protocol are simulated on Mininet. The POX controller with Mininet programming feature for creating smart topologies was chosen. In this research, the SDN network using Dijkstra’s algorithm, Bellman-Ford algorithm, extended Dijkstra’s algorithm and Floyd Warshall Algorithm were implemented. The quality factors of SDN created by using four algorithms are measured in terms of delay, jitter, latency, packet loss, transmit, received, throughput, and bandwidth based on experimental results and European Telecommunications Standards Institute (ETSI) data. The performance parameters of SDN network topology created using Dijkstra’s, bellman ford, extended Dijkstra’s, and Floyd Warshall algorithms were compared and the experimental results showed that Bellman-Ford algorithm is better in terms of performance parameters than the other three algorithms.

Dynamic Upgrade to SDN From a Global Perspective: Model and Its Heuristic Solutions

Software Defined Network (SDN) has been considered as one of the most promising next-generation network solutions due to its network programmability. However, the upgrade from legacy IP network to pure SDN network is in general a gradual process. From a global perspective, a dynamic upgrade strategy should not only aim to pursue the local goal at each step, but also strive to optimize the final global solution when the upgrading process terminates. This raises three essential questions: which switches to upgrade, when to upgrade, and how to deploy controllers. Due to the interaction between the local goals at intermediate steps and the global goal at the final step, answering these questions altogether from a global perspective is challenging. In this paper, we study the dynamic SDN upgrade problem from a global perspective and answer these three questions altogether. We formulate the problem as a dynamic optimization problem that optimizes the global and local goals at the same time. We then propose two new formulations to combine the global and local goals, and two heuristic algorithms to solve them, respectively. We evaluate the proposed model and algorithms on realistic network topologies. The results show their feasibility and superiority.

ML-based pre-deployment SDN performance prediction with neural network boosting regression

Software defined networking (SDN) has been proposed as an effective approach to improve network management efficiency and increase network intelligence in various networks. However, configuring and deploying suitable SDN functionalities is challenging. To overcome these obstacles in SDN network deployment, machine learning models have been introduced to monitor and predict the quality of service (QoS) in an SDN-enabled network. However, most existing studies are applicable only to deployed SDN networks with specific network topologies. In this study, a prediction-based SDN network configuration and deployment scheme is proposed for unseen network topologies before the actual deployment of SDN functionalities. In addition, a machine learning-based pre-deployment SDN performance prediction problem is formulated, and a neural network boosting regression model (i.e., NNBoost) is proposed as the solution. Numerical experiments demonstrate that NNBoost outperforms other machine learning algorithms, including deep learning methods, proposed in the literature, on datasets generated with both real-world and synthetic network topologies, compared with random forest (RF), AdaBoost, XGBoost, LightGBM, deep neural network (DNN) and long short-term memory (LSTM). It is found that NNBoost achieves lower root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) results for the maximum and mean values for the Round-Trip Time (RTT), Switch-to-Controller (S2C) traffic, and Controller-to-Controller (C2C) traffic. It is also found that by adding extra training data samples generated with synthetic network topologies, NNBoost achieves a better prediction performance on the test data samples for real-world network topologies.

A survey of the characteristics of SDN, NFV and information security in IoT and 5G networks

Background, The 5G technology has emerged as a trusted source to meet the increased demand of Internet of Things (IoT) devices, in addition to enabling Internet connectivity at high broadband speeds. Another important feature of 5G is the use of techniques such as Software Defined Networking (SDN) and Network Functions Virtualization (NVF), mechanisms responsible for performing network configurations through software, in addition to massive control and management of devices using the network configuration functions or device virtualization. Results, The concern with information security in the 5G network is increasing, as cybercriminals try to access important data that is transported over the network, since the demand for connected IoT devices will be greater, thus allowing for several possibilities of attacks. The understanding of possible threats and attacks is necessary, so that new measures are taken against cybercrimes presented in the 5G and IoT networks. Conclusions, This paper aims to elucidate some conceptions of what 5G technology is and the use of IoT in this network, contextualizing the SDN and NFV techniques to allow the configuration of the functionality and management of the network by software. In addition, concerns will be reported about possible information security attacks that may occur in 5G.

DDOS ATTACK DETECTION USING HYBRID (CCN AND LSTM) ML MODEL

LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks) are two types of deep learning algorithms; by combining the strengths of LSTM and CNN, researchers have developed deep learning models that can effectively detect SDN (Software-Defined Network) attacks including Distributed Denial of Service. These models effectively analyze network traffic, encompassing temporal and spatial characteristics, resulting in precise identification of malicious traffic.In this research, a hybrid model composed of CNN and LSTM is used to detect the DDoS attack in SDN network. Where the CNN component of the model can identify spatial patterns in network traffic, such as the characteristics of individual packets, while the LSTM component can capture temporal patterns in traffic over time, such as the timing and frequency of traffic bursts. The proposed model has been trained on a labeled network traffic dataset, with one class representing normal traffic and another class representing DDoS attack traffic. During the training process, the model adjusts its weights and biases to minimize the difference between its predicted output and the actual output for each input sample. Once trained, the hybrid model classifies incoming network traffic in the dataset as either normal or malicious with an initial accuracy of (78.18%) and losses of (39.77%) at the 1st epoch till it reaches an accuracy of (99.99%) with losses of (9.29×10-5) at the epoch number 500. It should be mentioned that the hybrid model of CNN and LSTM for DDoS detection is implemented using Python Anaconda platform with an ETA 28ms/step.