Abstract: The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI’s predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks.

Abstract: Software-Defined Networking (SDN) is a promising solution for large-scale network management that offers extensive opportunities for optimization. However, the centralized control inherent in SDN also exposes networks to security threats, notably Distributed Denial of Service (DDoS) attacks. To address these challenges, machine learning (ML) techniques have emerged as potent tools for anomaly detection and mitigation. This paper proposes a novel approach for traffic classification within SDN environments that distinguishes between benign and malicious traffic using supervised ML techniques. This study introduces a unique dataset tailored for DDoS attack detection, overcoming the limitations of existing datasets, such as unrealistic topologies and lack of public availability. Benchmarking against the CICDDoS2019 dataset validated the efficacy and relevance of the custom dataset. This research has significant implications for real-world applications, offering improved capabilities for detecting and mitigating DDoS attacks in SDN infrastructure. Experimental results demonstrated the effectiveness of the proposed random forest model, achieving a remarkable accuracy of 98.97% and a minimal False Alarm Rate (FAR) of 0.023. These findings underscore the potential of ML-based approaches in enhancing network security and resilience against DDoS attacks in SDN environments, paving the way for future advancements in network-defense strategies.

Abstract: Network infrastructure management has been completely transformed by Software-Defined Networking (SDN), allowing for centralized control and programmability. The significant challenge in SDN is to provide optimal routing decisions based on real-time network performance metrics. In this work, it is proposed to have Gated Recurrent Unit (GRU) based traffic prediction to dynamically adjust link weights in SDN. It facilitates real-time adaptation to traffic changes and optimal routing decisions for improved Quality of Service (QoS). This work is to provide optimal routing in SDN by leveraging the capability of GRU to predict future traffic based on network performance metrics sampled at different time sequences. It enables the network to adapt the changing traffic patterns in real-time. The predicted traffic is then used to determine the edge weights between links in the network, which are updated dynamically to reflect changes in the network. When an outbound packet is received, it can be routed optimally by selecting a path with less traffic, thereby reducing latency and improving the QoS of routing the packets. The simulation results reveals that the proposed methodology has the potential to reduce delay up to 9.16% and jitter up to 32.31%, coupled with significant throughput enhancements up to 26.81% compared to the default shortest path. These quantitative findings highlight its effective contribution towards optimizing network performance and QoS.

Abstract: The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

Abstract: Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.

Abstract: Nowadays, the security of ICT infrastructure, in particular regarding critical installations from the point of view of state security, it is becoming an increasing challenge nowadays. The uncertain situation in the international security arena does not minimize threats, but on the contrary increases the perception of risks and causes an increase in the number of threats. This state of affairs increases the risk of new, unexpected threats, in particular coming from cyberspace and targeting industrial infrastructure. In order to reduce the level of these threats, the Consortium consisting of EXATEL, GAZ-SYSTEM and the Rzeszów University of Technology has undertaken activities aimed at developing a new Polish cybersecurity technology, based on SDN software-defined network solutions, which is to enable reliable, fast and more precise management of ICT network resources. As a result, he will allow it. This allows for earlier detection of a potential attack, its visualization, understanding of the attack's input vectors and its scope, and take effective defensive measures.

Abstract: Software-Defined Networks (SDN) provide a new networking solution that decouples the control and data planes. The SDN controller as core component of the control plane manages and governs devices in the topology using the OpenFlow signalling protocol. The centralized construction of SDN leads to scalability issues. This paper analysis the number of switches impact on SDN controller performance. The performance metrics considered to measure the control plane quality of SDN controller are Topology Discovery Time (TDT) and Number of Overhead Messages (NOM). The paper focuses on two java based controllers which are OpenDaylight (ODL) and Open Network Operating System (ONOS). The simulation results show that increasing of openFlow switches number impacted on SDN performance of ODL and ONOS. However, the results show that ONOS outperform ODL in terms of performance metrics when the number of switches increases.

Abstract: Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.

Abstract: Data security in the Internet of things (IoT) is often implemented by means of encryption, which can be burdensome for some entities. We propose in this paper a solution based on routing, by which data are forwarded only to entities that are intended to receive them. An IoT network can be seen as a partial order of equivalence classes of entities, and each entity can be labeled according to the position of its equivalence class in the partial order. The partial order can be constructed according to requirements of secrecy (or confidentiality), integrity and conflicts. Routing tables among entities can be compiled by using the labels. The method is demonstrated in this paper for Software defined networking (SDN) routers and controllers. We propose a centralized IoT architecture with a cloud structure using SDN as networking infrastructure, where storage entities (i.e. cloud servers) are associated with application entities. A small ‘hospital’ example is shown for illustration. Procedures for network reconfigurations are discussed. We also demonstrate the method for the normal case where different partial orders coexist among a set of entities. The method proposed does not impose an overhead on the normal functioning of an SDN network, since it requires calculations only when the network must be reconfigured, because of administrative intervention or policies.

Abstract: Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.

Abstract: Software Defined Network (SDN) merupakan sebuah konsep pendekatan baru dalam jaringan untuk mendesain, membangun serta mengelola suatu jaringan komputer. Konsep ini melakukan pemisahan terhadap Data Plane dan Control Plane. Dalam konsep SDN ini terdapat suatu komponen penting yang bertanggung jawab terhadap segala aturan dalam pengelolaan dan pendistribusian informasi terhadap seluruh perangkat jaringan yaitu Controller. Karena peran Controller yang penting maka performa dari Controller perlu diuji sehingga dapat mengetahui kemampuan dari Controller yang digunakan. Dalam penelitian ini dilakukan perbandingan analisis nilai Quality of Services (QoS) terhadap implementasi SDN menggunakan Controller Floodlight dan Ryu dengan menjalankan topologi linear dan mesh dalam jumlah Switch yang beragam mulai dari 4, 8, 12 dan 16 Switch. Selama pengujian berlangsung dari node sumber ke node tujuan yang sama juga dialiri variasi background traffic mulai dari 50 hingga 200 Mbps. Hasil yang didapatkan yaitu Controller Ryu memiliki nilai QoS yang lebih baik dari floodlight pada semua topologi yang diujikan, nilai latency dan jitter pada floodlight lebih tinggi dari ryu serta cenderung meningkat pada traffic 100 Mbps Pada throughput, ryu memiliki nilai lebih tinggi dengan kisaran 856-933 Kbps. Sedangkan pada packet loss floodlight lebih tinggi sementara ryu hanya memiliki rata-rata packet loss sebesar 0,5%. Namun pada pengujian hanya pada jumlah switch, floodlight menjamin dalam tingkat respons serta pengelolaan data yang besar di dalam arsitektur jaringan SDN.

Abstract: In recent years, the data center network has improved its rapid exchanging abilities. Software Defined Network (SDN) architecture adds functionality to the node globally by means of separation of the control and data plane. Switches and routers are network devices that are inflexible to deal with different loads of traffic in a network and experience limitations on routing and load balancing. Data centers are utilized whenever high user traffic is encountered. Network operators manage the network by controlling the traffic and providing optimal resource allocation strategies to their applications. A path optimization algorithm is proposed to increase the performance of SDN. The proposed model's efficiency is demonstrated and simulated.

Abstract: This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems.

Abstract: Abstract Network slicing stands out as a crucial feature of software-defined networks (SDN), playing a pivotal role in the deployment of 5G networks amid the current era of technological advancement. This capability empowers operators to manage multiple virtual networks atop a shared physical infrastructure. Service providers can thereby segment their network resources into distinct logical networks, each tailored to meet the specific requirements of diverse users or services. As 5G deployment progresses, network function virtualization (NFV) and software-defined networking (SDN) are poised to steer the implementation of network slicing. In this review, I provide an overview of SDN in the context of 5G, exploring the role, motivation, and recent advancements in network slicing. The review further delves into the application scenarios of SDN within network slicing for 5G. The proposed architecture for network slicing in software-defined networking (SDN) for 5G networks encompasses three key usage scenarios: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). Conclusively, this report outlines challenges and suggests future research directions focused on network slicing in the context of 5G.

Abstract: Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.

Abstract: Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.

Abstract: In Software-Defined Networking (SDN), Intrusion Detection Systems (IDSs) are crucial for enhancing network security. These systems analyze and detect network anomalies dynamically, making SDN environments more responsive to emerging threats. The BAT-MC model has demonstrated effectiveness in improving network traffic analysis within SDN. This model integrates bidirectional context understanding and attention mechanisms, combining Bi-directional Long ShortTerm Memory (BiLSTM) and Attention layer with multiple convolutional layers. The proposed system offers precise anomaly detection and improved traffic optimization within the SDN infrastructure. Traditional IDSs relying on the NSL-KDD dataset face accuracy and scalability constraints, even with machine learning techniques. Moreover, manual feature engineering complicates the challenge due to the increasing diversity and complexity of network traffic. To tackle these issues, BAT-MC employs advanced deep learning techniques by seamlessly integrating BiLSTM and attention mechanisms, eliminating the need for manual feature design and significantly improving intrusion detection capabilities. To address existing dataset limitations, the In-SDN dataset is used, aiming to enhance system performance in intrusion detection when combined with the BAT-MC model and ensemble methods. Through a comprehensive approach, the goal is to improve accuracy, scalability, and adaptability, achieving an impressive $\mathbf{8 6 \%}$ performance in safeguarding SDN environments from various potential threats.

Abstract: Recent years have seen a drastic increase in the varieties and intricacies of network systems which are made up by rapid improvements that follow mobile connections as well as the internet. These systems are becoming increasingly complicated and more sophisticated solutions must be developed to ensure close cooperation, control, activation, and optimization of network structures. But conventional networks, due to their programmatically distributed functionality are a challenge when incorporating machine learning methods for network management. With the emergence of Software Defined Network (SDN), there is a new dimension for introducing intelligence in networks. Particularly, three core characteristics of SDN – unity management, global network visibility, and dynamic rule update - support seamless integration of machine learning technologies. This review provides a comprehensive overview of the literature on machine learning algorithms in SDN frameworks, presenting an extensive survey of this area. The paper systematically describes different machine learning algorithms that have been employed in SDN domains, thereby revealing their implementation opportunities as well as advantages and peculiarities. Furthermore, the review provides an overview of related works and background on SDN-based machine learning approaches for readers to gain a broad understanding of ongoing research in this field. While the topics covered extend beyond algorithmic research, it also challenges integration issues of machine learning into SDN and provides a wider scope. This review aims to be a reliable source of information for researchers, practitioners, and industry experts interested in Software Defined Networks and machine learning applications on network optimization and management.

Abstract: Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field.

Abstract: We propose a novel foundation for a mathematical framework to model and understand the dynamic behavior of Software-Defined Networks (SDNs), enabling the development of new algorithms for reliable cyberattack detection. SDNs, increasingly adopted in critical military networks, present unique challenges to traditional statistical-based detection methods due to their rapidly changing components and configurations. The December 2015 cyberattack on the Ukrainian power grid, where attackers exploited the flexibility of SDNs to disrupt operations, underscores the urgency of developing robust detection mechanisms. Our approach combines dynamical systems theory with persistent homology, a tool from topological data analysis, to overcome the limitations of current methods and address the dynamic nature of SDNs. This innovative approach promises to revolutionize cyberattack detection in SDN environments, offering enhanced protection for critical infrastructure.

Abstract: Purpose: The general objective of this study was to examine Software-Defined Networking (SDN) for efficient network management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Software-Defined Networking (SDN) for efficient network management. Preliminary empirical review revealed that SDN offered significant advantages in enhancing network agility, scalability, and operational efficiency. By centralizing network management functions and abstracting network control, SDN enabled dynamic resource allocation and optimized traffic flows. However, challenges such as security vulnerabilities, interoperability issues, and the need for specialized skills were identified. Successful SDN implementation required careful planning, rigorous testing, and strategic integration with existing IT infrastructures. Future research recommendations included further exploration of SDN technologies, evaluation of their impact on network performance and security, and the development of best practices for deployment and management to maximize benefits. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) Theory may be used to anchor future studies on Software-Defined Networking (SDN). The recommendations drawn from the study on Software-Defined Networking (SDN) for Efficient Network Management focused on enhancing theoretical frameworks, improving practical implementations, informing policy development, promoting industry collaboration, addressing security concerns, and facilitating stakeholder engagement. These initiatives aimed to strengthen SDN adoption and implementation by refining theoretical models, advocating for supportive policy environments, fostering industry partnerships, addressing security challenges, and engaging stakeholders throughout the deployment process. By integrating these strategies, the study sought to optimize network management efficiency and promote sustainable technological advancements in SDN.

Abstract: Software-Defined Networking (SDN) provides the flexibility to dynamically manage network paths, facilitating efficient traffic flow and mutipath routing, thereby improving network resiliency to congestion. This study investigates three distinct SDN-enabled routing strategies: shortest path routing, Equal-Cost Multi-Path (ECMP) routing, and bandwidth-based multipath routing. The comparative result analysis across these three routing strategies revealed that bandwidth-based routing consistently outperforms Shortest Path and ECMP routing across key performance metrics. The study found that Bandwidth-based routing maintains lower delay and jitter, indicating its superior ability to manage network traffic efficiently even as data flow increases. Furthermore, it demonstrates a more modest increase in packet loss, underscoring its effective congestion management. These findings suggest that Bandwidth-based routing provides a more reliable and efficient network performance, particularly in high-traffic conditions, making it a preferable solution for SDN implementations seeking to optimize data flow and network stability.

Abstract: The ever-expanding Industrial Internet of Things (IIoT) demands highly resilient and secure network architectures. While the execution of Software-Defined Networking (SDN) tackles the issue of heterogeneity in IIoT networks by the provision of centralized control and programmability, its scalability is exacerbated in large-scale IIoT deployments. This paper highlights the vitality of a distributed control plane architecture for SDN to overcome the scalability issues, thereby bolstering its efficiency. Furthermore, we propose a (t, n) threshold paradigm which ascertains that only authorized entities can access private data. The proposed protocol promotes reliability and data privacy, paving the way for secure operation within convoluted IIoT networks.