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Summary: Underwater wireless sensor networks (UWSNs) based on autonomous underwater vehicles (AUVs) have become the standard technology for underwater search tasks with the advent of new underwater information and communication technologies (ICTs). It is also vulnerable to threats and malicious attacks because of its inherent characteristics, open acoustic channel and hostile underwater environment. The purpose of this research is to develop a secure data transfer (SDF) system based on software‐defined networking (SDN) architecture with cooperative searching scheme (CSS) (SDN‐SDF‐CSS) for AUV‐based UWSN. This part employs data, local control and the primary control layer to provide a scalable SDN‐based architecture for the AUV‐based UWSN. To accomplish the underwater search operation, the data layer is primarily deployed to gather AUVs (G‐AUVs) and store AUVs (S‐AUVs). In order to schedule the AUV, the clustering process takes place based on priority ranking with respect to the average power of each cluster. Then, the CSS is developed, and it is performed in the data layer. The hierarchical localization framework (HLF) can be used to locate each AUV's location within the data layer, which is a necessary step in deploying the cooperative searching model. Finally, for an efficient data transfer, the communication model is deployed in the data layer. UWSNs are vulnerable to various malicious attacks (such as bad‐mouthing attacks, on–off attacks, blackhole attacks and wormhole attacks) because of the high bit error rate and unstable optical/acoustic channels in the underwater environment. To overcome this, an SDF technique is used, which involves isolating the attacker node from the data layer. The suggested SDN‐SDF‐CSS model is implemented on the MATLAB platform, and its performance is evaluated using various evaluation metrics, both with and without attacks. As a result, the proposed SDN‐SDF‐CSS model has achieved better performance and proved its superiority in the UWSN environment. [ABSTRACT FROM AUTHOR]

In order to address the load imbalance problem, a successful technique known as Fractional Political Optimizer (FPO)-based switch migration is modeled. The suggested FPO is created by integrating the Political Optimizer (PO) with Fractional Calculus (FC). DQN receives the controller's initial load value in order to generate the anticipated load. By calculating the load value based on CPU utilization, controller maximum capacity, and input packet volume, load imbalance issues are identified. Switch migration is carried out using FPO based on fitness measure, which takes into account migration factors, such as migration cost, projected load, controller capacity, and load balancing rate (LBR). To complete the switch migration procedure, the load on the controller is assessed using the data packets and a threshold value. For the NSFNet topology, the proposed FPO-based switch migration approach achieved the following results: 17.286 kbps, 0.1191 s, 48.797, −0.238, 0.1231 s, and 5.0391 kbps, respectively. [ABSTRACT FROM AUTHOR]

Through the integration of Network Function Virtualization (NFV), Software-Defined Networks, cloud service providers can realize cost reduction, performance enhancement, and support for advanced application scenarios. Within this framework, Service Function Chain (SFC) has emerged as a pivotal approach for delivering network services, offering flexibility and cost-effectiveness in provisioning Virtual Network Functions (VNFs) dynamically and elastically across a network of physical devices. This paper concentrates on optimizing VNF placement within NFV-enabled networks to elevate the number of SFC Requests (SFCRs) successfully processed. Emphasizing the often-overlooked factors of time-varying workloads and VNF sharing via multi-tenancy technology, we present a novel Integer Linear Programming (ILP) formulation for the strategic placement of VNFs. To effectively solve this ILP, we introduce a Throughput Optimization Heuristic Solution (TOHS), comprising an algorithm based on correlation for assigning SFCRs to nodes, alongside a VNFR aggregation algorithm aimed at optimizing node resource utilization by consolidating VNFRs requiring identical VNF types. Through detailed numerical simulations and comparative analysis with existing literature, we demonstrate that TOHS approaches the performance of ILP solvers for small-scale problems, outperforming other solutions in diverse scenarios. Specifically, TOHS achieves higher network throughput and more efficient utilization of network resources, underscoring its efficacy in practical NFV-enabled network deployments. [ABSTRACT FROM AUTHOR]

The rapidly growing number of Internet of Things (IoT) devices has led to a rise in data transfers, which has raised security concerns. Due to the devices' limited processing capabilities and vulnerability to many cyber attacks, securing IoT communications is challenging. Security threats, especially Distributed Denial of Service (DDoS) attacks, take a toll on the network in the form of increased communication overhead. Hence, a centralized unit is required to detect DDoS attacks in IoT networks at the earliest. Software-Defined Networking (SDN) promises a potential solution for better network traffic management and data flow. This paper presents a machine learning-based ensemble model for the detection of DDoS attacks in IoT networks using SDN. The proposed model employs a multi-step approach utilizing various Machine Learning (ML) algorithms. The proposed Ensemble Model (EM) combines Logistic Regression (LR), k-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra-tree (ET), AdaBoost, and XGBoost, with XGBoost as the final estimator classifier. Various metrics, including sensitivity, specificity, precision, accuracy, and others, derived from the confusion matrix, evaluate the proposed model's performance. The EM demonstrates superior performance during comparative analysis with state-of-the-art schemes, with a classification accuracy of 99.8%. Furthermore, the paper evaluates the model based on Receiver Operator Characteristic (ROC) curves, showing its superiority in True Positive Rates (TPR) compared to False Positive Rates (FPR). The AUC analysis supports the EM's effectiveness. Cross-validation results further validate the model's robustness, with a mean accuracy of 97.92%. [ABSTRACT FROM AUTHOR]

Traditional network administration required manual programming of routing policies and related parameters on specific routers and switches, which was expensive. Therefore, software-defined networking (SDN) technology has been introduced, which has boosted flexibility and decreased hardware development costs by centralizing network management. Since intrusion detection is vital in the SDN environment, this centralized architecture makes information security vulnerable to network threats. To evaluate and recognize these attacks, many researchers have recently adopted cutting-edge approaches like machine learning. However, most of these methods are not very accurate and scalable. To address this issue, this paper proposes an EfficientNetV2-RegNet-based effective deep learning technique. It effectively extracted the network features and classified the intrusions in SDN-based IoT (Internet of Things). Afterwards, an effective mitigation process was performed by a remote SDN controller to mitigate the assaults and reconfigure the network resources for trusted network hosts. Furthermore, the Conditional Generative Adversarial Network (CGAN) based data augmentation approach efficiently tackles the data imbalance issue. The most recent realistic datasets, named InSDN and IoT-23, were utilized to train and assess the presented framework to validate its efficiency. The results of the experiments demonstrated that the suggested system surpassed competitors in identifying various attack types and achieved 99.53 and 99.56% accuracy for IoT-23 and InSDN datasets, correspondingly. [ABSTRACT FROM AUTHOR]

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks. [ABSTRACT FROM AUTHOR]

The Software-Defined Network (SDN) remains the innovative model that assists in satisfying the new application requirements of future networks. However, destructive attacks, particularly Distributed Denial of Service (DDoS) attacks, are aimed primarily at the SDN control panel, presenting a significant risk to network security. The traditional approaches for DDoS attack detection exhibit several limitations including interpretability challenges, overfitting problems, and false predictions. To mitigate these drawbacks, the research proposed a Pipit Flying Fox Optimized Deep Neural Network (PPF-DNN) and Intelligent Pipit Forage Optimized-Attentional Convolutional Neural Network (IPFO-ACNN) model. The channel attention is employed to enable the network to focus selectively on relevant information, improving the model’s sensitivity to subtle anomalies associated with DDoS attacks. By dynamically adjusting the attention weights across channels, the mechanism enhances the capability of the model to discriminate the normal network traffic and malicious patterns, which results in improved precision and reduced false positives. This approach harnesses the power of CNN to automatically extract hierarchical features from network data, enhancing the efficacy of the ACNN model in attack detection. IPFO and PPF algorithms integrate the unique strengths of bio-inspired algorithms, creating a synergistic approach for efficient model parameter tuning. In the case of Training Percentage (TP) 80, the accuracy, sensitivity, and specificity of the IPFO-ACNN model are evaluated as 98.87%, 99.24% and 97.91%, respectively. Similarly, the PPF-DNN model’s performance is assessed as 98.49%, 92.50% and 98.55%, which presents a promising avenue for bolstering network security infrastructure, ensuring more robust DDoS detection and mitigation capabilities in an increasingly complex cyber threat landscape. [ABSTRACT FROM AUTHOR]

Advances in the Internet of Things (IoT) and underwater communications have led to extensive research in academia and industry in recent years to implement underwater applications. The successful operation of these applications depends on the use of efficient communication protocols and solutions. Given the dynamic and bandwidth-limited nature of underwater communications, the use of condition-aware routing techniques can mitigate some of these limitations. In this paper, we propose a multi-objective path computation approach for underwater IoT networks that aims to balance energy consumption and maximize network throughput. The proposed mechanism leverages the benefits of Software-Defined Networking (SDN) architecture to collect information about the coordinates of underwater nodes and then calculates the optimal paths to the destination node using a multi-objective mathematical model. Once the paths are calculated, the underwater nodes send data packets towards the destination based on the calculated paths. Simulation results demonstrate that the proposed solution increases the throughput of the network while balancing energy consumption compared to baseline methods. [ABSTRACT FROM AUTHOR]

Recent evolution in connected devices modelled a massive stipulation for network traffic resources and classification. Software-defined networking (SDN) enables ML techniques with the Internet of Things (IoT) to automate network traffic. This helps to reduce accuracy and improves latency. Problems by conventional techniques to categorize network traffic acquired from IoT and assign resources can be resolved through SDN solutions. This manuscript proposes a proposed network traffic classification technique on IoT with SDN called Gauss Markov and Flow-balanced Vector Radial Learning (GM-FVRL). With the network traffic features acquired from the IoT devices, SDN-enabled Gauss Markov Correlation-based IoT Network Traffic Feature Extraction is applied to extort relevant network aspects. Next, the flow-balanced radial-based ML model for network traffic categorization uses the relevant extracted network traffic features. With the aid of flow, the balanced radial basis function reduces the influence of noise due to distinct network flow. This helps to improve accuracy and minimize latency. Due to this, better precision and recall is ensured. Performance of our method has been evaluated utilizing a scheme using an SDN traffic dataset. The results show that our method classifies the network traffic with high classification accuracy and minimum latency, ensuring better precision and recall. [ABSTRACT FROM AUTHOR]

By decoupling the data plane and the control plane of networking devices, Software Defined Network (SDN) enables the control plane devices to obtain the abstract view of the entire network and accomplish network functions as necessary. In SDN, control plane device, known as SDN controller is responsible for managing and setting rules for the data plane devices (SDN switches). Multiple SDN controllers are often used to formulate distributed controller architecture for geographically larger networks where different sets of switches are connected to and managed by different controllers. Due to network dynamics, any particular controller can become overloaded which results in increased response time and thereby reduce the end-user quality of service. To tackle the controller overload issue, in this paper, an effective and efficient load balancing framework is proposed. Most of the previous works in this domain focus on precise load balancing; therefore many load shifting (switch migration) operations are required and consequently degrade the end-user QoS. Additionally, beneath a certain load, CPU performs almost equally well; therefore load balancing before reaching a certain controller's load seems unnecessary and questionable. Unlike most of the previous works, our algorithm balances the load loosely in a proper distributed controller SDN architecture. The load balancing framework uses five key modules to perform faster switch migrations to balance the load. Number of switch migrations, selection time performance, and load balancing precision are the performance metrics for evaluation and comparison for this work. The resultant load-balancing solution has successfully shown its potency in terms of reducing the number of switch migrations and the selection time compared to some recent and notable works in this domain. Moreover, a completely new functionality named Switch Restoration is introduced in our load balancing solution to optimize the network further. [ABSTRACT FROM AUTHOR]

There has been a discernible rise in the growth and progress of the Internet, networking, and mobile communication. The complexity of networking systems has increased due to the advancements in devices, resources, and infrastructure. A novel and developing network technology called software-defined networking (SDN), gets beyond the drawbacks of conventional networks and gives networking systems intelligent control. While SDN is the most adaptable and promising network management control solution, its implementation also introduces a number of new security risks. There is a need to deploy networking systems intelligently to manage, optimize, and organize these complex systems. Machine learning (ML) approaches have been extensively utilized to detect many attacks, and an ML technique may assist the network administrator in taking the necessary precautions to avoid intrusions. In SDN, ML techniques are used to manage the network and make Network Intrusion Detection Systems (NIDS) detect network attacks like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. This paper comprehensively surveys ML and Deep Learning (DL) algorithms and techniques used for intrusion detection in SDN. Different studies are categorized accordingly, such as supervised, unsupervised, and deep learning models. Research studies are compared in the form of a table, and learned lessons are also leveraged for each category. Finally, this survey presents the key challenges faced in implementing different intrusion detection techniques in SDN before comprehensively highlighting future research directions. [ABSTRACT FROM AUTHOR]

With the surge in mobile data traffic, leveraging Wi-Fi has become pivotal to alleviate the strain on LTE networks. An essential challenge lies in selecting the optimal Wi-Fi access point during Handover (HO), impacting Quality of Experience (QoE) and connection stability. This study introduces an innovative approach to enhance VoIP call Quality of Service (QoS) during HOs from LTE to Wi-Fi. Our contribution integrates two Mean Opinion Score (MOS) prediction models, multilayer perceptron (MLP) and recurrent neural networks (LSTMs) into the software-defined network controller (SDN), optimized by the Grey Wolf Optimizer (GWO). This solution provides accurate predictions for the SDN to forward mobile devices (MEs) and traffic to the nearest optimal Wi-Fi access points. Simulation results affirm the effectiveness of our approach. The LSTM-based approach performs well in the long run and provides better stability in terms of QoS parameters, especially when the metric of choice for mobile users is based on the Signal-to-Noise and Interference Ratio (SNIR), rather than the Received Signal Strength (RSSI) indicator. These results explore opportunities for the real-world deployment of the implemented models, particularly in urban areas experiencing cellular network congestion. [ABSTRACT FROM AUTHOR]

Recent ground-breaking research in the Internet of Things (IoT) and drone technology has brought about revolutionary advancements in automation, remote sensing, and remote operations. Drones have gained massive popularity in IoT applications, like remote monitoring and task automation. Consequently, an innovative business model called Drones-as-a-Service (DaaS), which aims at leasing out drones for commercial use, is gaining momentum in the market. This spike in demand for drones is credited to their ease of deployment, flexibility of operation, and risk-free functioning. To fully utilize the advantages that drones provide and further delve into the flourishing field of drone research, it is essential to acquaint ourselves with the scope of this field and understand the capabilities of a drone. The methodology employed in this survey paper encompassed a thorough literature review to establish a foundation, followed by the design and implementation of a comprehensive survey instrument. As a result, this survey provides a means for the reader to explore diverse drone research topics, ranging from basic drone types and deployment architectures to complex drone networking and communication systems. Intelligence-based enhancements for drone communications and Blockchain-based security solutions have also been discussed. We have conducted detailed comparisons between different classes and architectures of drones, drone communication protocols (WiFi, LTE, 5G, satellites), and drone-enabled 5G networks (Ad hoc networks and Software Defined Networks). Blockchain-based and Intelligence-assisted solutions for 5G-related drone network security concerns have also been discussed. We also highlight the utility of drones in present-day Smart City Infrastructure, ranging across multitudes of applications, as well as in the Precision Agriculture domain and their future scope. Finally, we outline significant security challenges and cyber-attacks faced by drones and their countermeasures, as proposed in the state-of-the-art literature. In conclusion, we end this survey by discussing the future scope and directions of drone research. [ABSTRACT FROM AUTHOR]

Integrating Software-Defined Networking (SDN) with the Internet of Things (IoT) simplifies the management of IoT devices; however, it introduces security challenges. Adversaries may manipulate forwarding rules to redirect communication, compromising user security. Additionally, the centralized nature of SDN-enabled IoT networks poses a single point of failure during master controller failure. To address these issues, we present SDBlock-IoT, a distributed SDN architecture based on blockchain technology. This ensures increased resiliency in the event of master controller failure. Our proposed model considers response time and resource utilization of equal controllers, ensuring the most suitable controller assumes the role of master controller. We enhance the integrity of OpenFlow forwarding rules through the Smart Agent and SC, which validate whether a flow is registered on the blockchain or not. The Smart Agent verifies forwarding rules for every new flow request. We conducted experiments on hardware SDN switches using a Ryu OpenFlow controller and a private blockchain, demonstrating the effectiveness of our approach. Evaluation results indicate that SDBlock-IoT outperforms existing solutions in terms of flow verification time, controller recovery time, CPU utilization, and transaction costs. [ABSTRACT FROM AUTHOR]

The routing protocol for Vehicular Ad Hoc Networks (VANETs) uses hop-count as a routing metric to determine the shortest route from source to destination. The presence of unstable links in the selected route may cause packet failures, resulting in an increase in packet retransmissions, causing network congestion, increasing total end-to-end delay, and reducing throughput. Thus, route selection based on hop count may not always be optimal in terms of route link quality. In Software-Defined Vehicular Networks (SDVN), the global view of link-state information for the vehicular networks is created at the controller; thus, a better route can be computed. Our study proposes an SDVN routing technique that considers link quality while determining the route. Our proposed framework examines the link quality using two metrics: Expected Transmission Count (ETX) and Expected Transmission Time (ETT), and this information is used by the controller to compute the flow rule. The routing performance of the proposed scheme is compared to traditional VANETs and other existing works with the help of network simulations using the NS-3 simulator. The obtained simulation results for our proposed scheme outperform in terms of performance metrics such as packet delivery ratio, average throughput, and end-to-end delay. [ABSTRACT FROM AUTHOR]

While software-defined networks (SDNs) were introduced to enhance the performance of traditional networks, they brought forth significant challenges, such as lack of scalability, low reliability, and the presence of a centralized controller as a single point of failure. This led to the emergence of distributed software-defined networks (DSDNs). Despite the numerous advantages of DSDNs, there are challenges, with improper traffic distribution identified as the primary issue, significantly affecting its performance and efficiency, resulting in increased latency, reduced throughput, and migration costs. This article introduces a novel approach called MBL-DSDN (micro-clustering bidirectional LSTM-DSDN) to balance server traffic distribution. The improvement of quality of service (QoS) parameters and optimal load distribution in DSDNs is accomplished by utilizing two modules: micro-clustering (MC) and a prediction module based on the B-LSTM method. The Davies–Bouldin (DB) index from the MC module is utilized to enhance the accuracy of clustering results. Since the B-LSTM prediction module possesses suitable memory to store all relevant past and future features with high precision, it is suitable for selecting the best controller with the lowest migration cost and response time. Moreover, the proposed method reduces the synchronization cost between data and controller layers, leading to reduced response time and, ultimately, lower processing costs and increased load balancing. In comparison with similar methods, as a result of multiple simulations, migration count, migration cost, and response time parameters have improved by 31%, 43%, and 40.5%, respectively. [ABSTRACT FROM AUTHOR]

Summary The growth of software‐defined networking (SDN) enhances network strength and provides flexible routing, especially in heterogeneous environments. Hence, an efficient framework is required for recent networks. Recently, hybrid SDN with the restricted deployment of SDN switches has been integrated with a conventional network that provides improved communication performance compared to traditional SDN systems. However, the recent hybrid SDNs lack effective link protection and optimal routing when used with complex topologies. Hence, this study presents a novel deep learning–based hybridized multi‐stacked autoencoder with the duo‐directed gated recurrent unit (MSAE‐DDGRU) for automatic link failure prediction in hybrid SDN. Moreover, a multi‐objective zebra optimizer (MO‐ZeO) is introduced to perform optimal routing by solving multiple routing constraints. The developed study is processed with the Python platform, and publicly available GEANT topology is utilized for the whole experimental process. Various assessment measures like accuracy, precision, sensitivity, packet loss, cost, maximum link utilization (MLU), policy violation rates (PVRs), packet delivery ratio (PDR), and delay are analyzed and compared with existing studies. The developed technique achieved an accuracy of 96%, precision of 92%, sensitivity of 93%, PDR of 99.4%, PVR of 0.0005, and delay of 1.2 s are obtained. [ABSTRACT FROM AUTHOR]

The 5G communication network is a promising technology that offers user-centric connectivity to customers, enabling quick, high-capacity, and latency-free access to many applications. Software Defined Networking Controller (SDNC) is an emerging technology that is used for increasing the security and maintaining the dynamic nature of mobile devices in latency and energy-assured 5G wireless communication. Different SDN topology open flow controllers are used in the literature for increasing the throughput and security of the network but they possess some drawbacks such as frequent location update (LU) overhead, high latency, and high energy consumption. This paper proposes a hexagonal software defined network (H-SDN) controller to overcome these challenges. The proposed H-SDN topology is used to preserve Intermediate Message Layer (IML) location during multimedia data transmission. A Core Switch (CS) updates an IML's location within clusters of cells, as long as it remains within the cluster. However, in the proposed method, there will be a location update on the server if the IML departs the cluster. Experiment results show that the proposed method can increase security, which in turn increases throughput, while reduces the location update costs, energy consumption, and transmission latency. [ABSTRACT FROM AUTHOR]

Summary Software‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity. [ABSTRACT FROM AUTHOR]

This study examines the problem of minimizing the amount and distribution of time delays or latencies experienced by data as they travel from one point to another within a software-defined network (SDN). For this purpose, a model is proposed that seeks to represent the minimization of the distances between network switches in proportion to the total nodes in a network. The highlights of this study are the proposal of two mixed-integer quadratic models from a fractional initial version. The first is obtained by transforming (from the original fractional model) the objective function into equivalent constraints. The second one is obtained by splitting each term of the fraction with an additional variable. The two developed models have a relationship between switches and controllers with quadratic terms. For this reason, an algorithm is proposed that can solve these problems in a shorter CPU time than the proposed models. In the development of this research work, we used real benchmarks and randomly generated networks, which were to be solved by all the proposed models. In addition, a few additional random networks that are larger in size were considered to better evaluate the performance of the proposed algorithm. All these instances are evaluated for different density scenarios. More precisely, we impose a constraint on the number of controllers for each network. All tests were performed using our models and the computational power of the Gurobi solver to find the optimal solutions for most of the instances. To the best of our knowledge, this work represents a novel mathematical representation of the latency density management problem in an SDN to measure the efficiency of the network. A detailed analysis of the test results shows that the effectiveness of the proposed models is closely related to the size of the studied networks. Furthermore, it can be noticed that the performance of the second model compared to the first one presents better behavior in terms of CPU times, the optimal solutions obtained, and the reduced Mipgaps obtained using the solver. These findings provide a deep understanding of how the models operate and how the optimization dynamics contribute to improving the efficiency and performance of SDNs. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Flow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT. [ABSTRACT FROM AUTHOR]

The rise of digital twin-based operational improvements poses a challenge to protecting industrial cyber-physical systems. It is crucial to safeguard digital twins while disclosing internals, which can create an increased attack surface. However, leveraging digital twins to simulate attacks on physical infrastructure becomes essential for enhancing ICPS cybersecurity resilience. This paper introduces an integrated intelligent defense framework called CyberDefender to study various attacks on digital twin-based ICPS from a four-layer perspective (i.e., digital twin-based industrial cyber-physical systems infrastructure layer, honeynet and software-defined industrial network layer, intelligent security platform layer, and smart industrial application layer). To demonstrate its feasibility, we implemented a proof-of-concept (PoC) solution using open-source tools, including AWS for cloud infrastructure, T-Pot for Honeynet, Mininet for SDN support, ELK tools for data management, and Docker for containerization. This framework utilizes an integrated intelligent approach to enhance intrusion detection and classification capabilities for digital twin-based industrial cyber-physical systems (DT-ICPS). The proposed intrusion detection system (IDS) combines two strategies to improve security. First, we present an innovative approach to identifying essential features using explainable AI and ensemble-based filter feature selection (XAI-EFFS). By using Shapley Additive Explanations (SHAP), we analyze the impact of different variables on predictive outcomes. Secondly, we propose a hybrid GRU-LSTM deep-learning model for detecting and classifying intrusions. We optimize the hyperparameters of the GRU-LSTM model by using a Bayesian optimization algorithm. The proposed method demonstrates excellent performance, outperforming conventional state-of-the-art techniques with an accuracy rate of 98.96%, which is a remarkable improvement. Additionally, it effectively detects zero-day attacks, contributing to digital twin-based ICPS cybersecurity resilience. [ABSTRACT FROM AUTHOR]

Both academics and the IT industry are now researching the Internet of Things and software-defined networks. They have received a number of criticisms in the SD-IoT due to their novelty. One of the 5 G technologies that makes it possible to construct complex, controllable, economical, and adaptive networks is software-defined networking (SDN). In contrast, edge computing (EC) uses data from sensors, network switches, or other devices to automatically do analytical computing rather than waiting for the data to be sent back to a centralised data repository. This article offers a study on feature selection using an ensemble of filter methods to create a lightweight IDS for SD-IoT edge devices that support OpenFlow in order to defend against such attacks. To create the ensemble of filter methods, three filter-based methods, namely Pearson's correlation coefficient (PCC), mutual information (MI), and Fisher's score, have been used. The features selected by this ensemble is sent to the ensemble of classifiers called stack of the classifiers that comprises of support vector machine (SVM) and K-nearest neighbour (KNN) at level '0' and logistic regression (LR) at level '1'. To check the effectiveness of the selected features, stack of the classifiers and individual classifiers are trained and tested with 'All' and 'Selected' features, and then their performances are compared. Two datasets, the BoT-IoT dataset and the TON-IoT dataset, were utilised to complete this work. The performance is compared under some performance measuring metrics, namely recall, accuracy, FAR, F1, precision, CKC, and prediction time. It has been discovered that classifiers perform better when trained with selected features rather than all the features. Also, it is discovered that stack of the classifiers with chosen features outperforms all individual classifiers, hence it is chosen for deployment in OpenFlow enabled edge devices of the SD-IoT data plane where it can identify and counteract threats in real-world settings. This offers the SD-IoT distributed attack detection approach. [ABSTRACT FROM AUTHOR]

Controller placement issue (CPI) in software-defined networks (SDNs) describes the controllers' number, location, and assigning of forwarding devices to controllers. Recently, mathematical formulations and algorithms have been proposed to solve various problems in SDNs and software-defined wide area networks. The comprehensive literature review can be divided into four groups according to objectives: (i) minimizing latency between forwarding devices or switches and their corresponding controllers, and minimizing latency between controllers, (ii) improving network resilience and stability, (iii) minimizing energy consumption and installation costs and (iv) using multi-objective approaches. In addition to the objectives of each research, the importance of this paper is to examine the CPI in terms of reducing the network search space in order to optimally place the controller and how to assign switches to the controllers. In this paper, first the mathematical formulations in previous studies will be examined and then, for solving CPI, the existing algorithms will be discussed. Different classifications of CPIs and related formulas/algorithms, descriptions, advantages and disadvantages will be separately provided. A comprehensive comparison of proposed approaches with their advantages and disadvantages in the summarized tables will be provided. Also, a comparative discussion of different statistics of CPIs will be presented in terms of some technical features such as objective-oriented problems and parameters in four categories, estimated environments, and efficient estimating factors in CPIs. Finally, we explained the future studies' challenges, problems related to CPIs, ideas and following orientations in this field. [ABSTRACT FROM AUTHOR]

In optical data center networks, the utilization of several software-defined network controllers has been implemented to enhance scalability and reliability. However, during dynamic variations in network traffic, static controller deployment often leads to load imbalance among the controllers. Previous studies have proposed dynamic switch migration as a solution to realize controller load balancing. However, the effectiveness of existing switch migration methods remains insufficient. The load balancing performance of controllers demonstrates limited enhancement following migration, and the migration operations themselves result in supplementary costs. As a result, the issue of switch migration efficiency remains unresolved. To tackle controller load balancing, this research introduces the intelligent controller load balancing (ICLB). ICLB incorporates multiple load metrics to evaluate controller load and selects the ideal target controller based on available resources, leading to enhanced load balancing effectiveness. Moreover, it selects switches while the minimizing migration process, effectively reducing supplementary expenses. Additionally, ICLB can simultaneously manage the workload of multiple controllers during a switch migration process, resulting in enhanced migration efficiency. The outcomes of simulations provide evidence that ICLB offers substantial improvements in controller load balancing performance, along with reduced migration expenses compared to existing solutions. [ABSTRACT FROM AUTHOR]

The escalating risks to network security, stemming from the continual evolution of network infrastructure and application systems, have significantly impacted system availability, with distributed denial of service (DDoS) attacks standing out as a particularly disruptive threat capable of overwhelming and even incapacitating systems. In response, this study introduces a comprehensive system (named uitSDD) designed to detect and mitigate DDoS attacks within software-defined networks (SDNs). Our proposed solution involves a multi-modules architecture operating as an intrusion prevent system, strategically engineered for seamless integration into the Controller device. The system’s functionality includes the periodic extraction of multiple parameters from flow entries on the OpenFlow switch, complemented by machine learning models, enabling the identification of abnormal packets infiltrating the system. Upon detecting an attack, the controller promptly dispatches policies to the OpenFlow switch, initiating the removal of malicious packets. This autonomous system employs common machine learning algorithms and real-time processes to detect and counteract attacks on OpenFlow Switches. The study evaluates the system’s performance using four distinct algorithms such as Decision Tree, K-Nearest neighbors (KNN), Random Forest, and support vector machine (SVM). Notably, when utilizing the CIC-DDOS2019 dataset to evaluate machine learning algorithms in detecting DDoS attacks, the Decision Tree algorithm exhibits the highest F1-score at 99.87%. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) has emerged as a transformative technology that separates the control plane from the data plane, providing advantages such as flexibility, centralized control, and programmability. This innovation proves particularly beneficial for Internet of Vehicles (IoV) networks, which amalgamate the Internet of Things (IoT) and Vehicular Ad Hoc Network (VANET) to implement Intelligent Transportation Systems (ITS). IoV provides a safe and secured vehicular environment by supporting V2V, V2I, V2S, and V2P. By employing an SDN controller, IoV networks can leverage centralized control and enhanced manageability, leading to the emergence of Software-Defined Internet of Vehicles (SD-IoV) as a promising solution for future communications. However, the SD-IoV networks introduces a potential vulnerability in the form of a single point of failure, particularly susceptible to Distributed Denial of Service (DDoS) attacks. This is because of the centralized nature of SDN and the dynamic nature of IoV. In this context, the SDN controller becomes a prime target for attackers who flood it with massive packet-in messages. To address this security concern, we propose an efficient and lightweight attack detection and mitigation scheme within the SDN controller. The scheme includes a detection module that utilizes entropy and flow rate to identify patterns indicative of attack traffic behavior. Additionally, a mitigation module is designed to minimize the effect of attack traffic on the normal operation, this is performed through analysis of payload lengths.The mitigation flow rule is set for specific traffic type if its payload is less than the threshold value to decrease the false positive rate. An adaptive threshold computation for all parameter values enhances the scheme's effectiveness. We conducted simulations using SUMO, Mininet-WiFi, and Scapy. We evaluated the system performance by using Mininet-wifi SDN simulation tool and Ryu controller for control plane. The system detects DDoS attack traffic within a single window by checking both entropy and flow rate simultaneously. The simulation results demonstrate the efficacy of our proposed scheme in terms of detection time, accuracy, mitigation efficiency, controller load, and link bandwidth consumption, showcasing its superiority compared to existing works in the field. [ABSTRACT FROM AUTHOR]

5G, the current generation of communication networks is based on the standards defined by 3GPP and other organizations (ETSI, ENISA, NGMN). These standards define virtual networks supported by three basic technologies, SDN, NFV, and Network Slicing. Virtual networks are primarily built using software and have clear advantages that appear to be reduced because of the corresponding loss in security due to the larger attack surface of this type of network. On the other hand, virtual networks can be made even more secure than hardware-based networks by leveraging the flexibility and adaptability of virtual functions and numerous articles have studied different aspects of their security. Current work goes from proposals for specific mechanisms to general studies of threats and defenses. Some of these are systematic literature reviews considering everything published on a specific theme. We prefer to analyze carefully selected papers considered significant and produce from them an overview of the status of the security of the network technologies used by 5G. After this analysis, we have found that although there are many studies of threats, they are not systematic and have confusions about concepts that may mislead implementers; we also found that the large variety of defenses can be confusing to designers. We have therefore conducted a critical analysis of threats and defenses to provide a clear perspective of how to secure these networks. Based on this perspective, we propose directions for research to improve or extend current defenses. We note that although virtual networks have special characteristics, they are examples of systems and much of the theory of systems security applies to them. [ABSTRACT FROM AUTHOR]

Software-Defined Networking stands as a pivotal technology in attaining the essential levels of flexibility and scalability demanded by pervasive and high-performance network infrastructure required for digital connected services. Nonetheless, its disaggregated and layered architecture makes it open to the time-based fingerprinting attacks. Besides, limited flow table capacity of the switches alleviates table saturation attacks. In this paper, an automated attacker tool called TASOS is proposed to infer flow table utilization rate, size and replacement algorithm. With this set of information, the attacker can conduct intelligent saturation attacks. Furthermore, a lightweight defense mechanism (LIDISA) for proactively deleting flow rules is described. A comprehensive simulation setup with different network conditions shows that the proposed techniques achieve superior success rate in diverse settings. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is known for its enhanced network programmability and adaptability, but maintaining strong safety precautions to protect against emerging cyber-attacks remains a constant issue. Since SDN has logically centralized control, an attack on the controller might paralyze the entire network. For this reason, intrusion detection is very crucial. Many academics have embraced state-of-the-art techniques to assess and identify these assaults. However, the majority of these approaches lack scalability and accuracy. Moreover, they had difficulties with restricted features, low efficiency, incorrect characteristics, and computing complexity. Therefore, to detect network vulnerabilities in SDN-based IoT networks, we developed a practical deep learning approach based on Res2Net and Elman Recurrent Neural Networks (ERNN) technique as a defense solution to detect security issues in SDN. This framework consists of multiple steps and starts by addressing the dataset's class imbalance issue with a Data Augmentation Generative Adversarial Network (DAGAN). Next, the Res2net and Enhanced Honey Badger Algorithm (EHBA) are used to extract and select features. This lowers the computational expense and lessens the possibility that the model would be misled by unsuitable and negative characteristics. Finally, an ERNN-based technique is used to detect and classify the intrusions in SDN. After seeing the network assaults, a practical mitigation framework is implemented to mitigate the network attacks. Three SDN IoT-focused datasets, InSDN, IoT-23 and ToN-IoT, are used in an experimental investigation to analyze the proposed framework's performance. The results of numerous trials show that the proposed method outperforms existing techniques regarding several constraints. [ABSTRACT FROM AUTHOR]

In software-defined networking (SDN), where software assumes a progressively prominent position in the network administration, it becomes imperative to emphasize the enhancement of data authentication and non-repudiation methodologies. The study presents a novel approach that incorporates Multi-Party Computation (MPC) into symmetric systems to enhance data authentication inside SDN settings. The use of MPC ensures the timely authentication of identities, prevention of repudiation, and maintenance of data accuracy, while simultaneously protecting the confidentiality of sensitive data and parameters. The evaluation of our technique's performance is accomplished through rigorous simulations utilising NS-3. The simulations yielded positive results in terms of packet delivery ratio, end-to-end delay, and throughput. The findings of the research illustrate that the proposed approach demonstrates consistent efficacy and reliability, even under conditions of heightened network node density. This study presents a complete methodology aimed at enhancing data authentication in SDN. This paper examines the increasing security challenges associated with cloud-based SDN solutions. This research demonstrates a significant progress in the quest for a secure and efficient SDN environment, setting new standards for data authentication and non-repudiation. [ABSTRACT FROM AUTHOR]

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management. [ABSTRACT FROM AUTHOR]

With the ongoing digital transformation, the requirement for seamless & agile network connectivity has increased. This has necessitated the transformation from traditional networking to Software-Defined Network. With SDN, the network resources/infrastructure can be made more robust and programmable to accommodate the varying demand. Software-Defined Network (SDN) is now being used by data centers, cloud computing environments, and service providers for efficient handling of network requests. The objective of SDN can be achieved through the performance of the controllers. We have considered POX, Ryu, and ODL controllers to evaluate the performance using the Mininet tool and D-ITG (Distributed Internet Traffic Generator) script. Performance is evaluated using custom topology i.e. Fat tree topology. Several performance metrics (such as Delay, Jitter, Bitrate, Packet rate, Loss-burst size, and Percentage of Packets dropped) are considered and the behaviors of the controllers are monitored during experimentation by flooding the switches with several data packets. It has been observed that the POX controller performs better compared to Ryu and ODL for the Fat tree topology. From the experimental results, it is observed that the average delay of ODL is approximately 50% more than the POX controller and approximately 80% more delay occurred in the Ryu controller in comparison to the POX controller. Similarly average packet dropped percentage of POX is 20% less than Ryu and ODL controllers. Also, it has been observed that the average jitter in POX is almost 50% less in comparison to ODL and 90% less in comparison to the Ryu controller. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDNs) have been growing rapidly due to their ability to provide an efficient network management approach compared to traditional methods. However, one of the major challenges facing SDNs is the threat of Distributed Denial of Service (DDoS) attacks, which can severely impact network availability. Detecting and mitigating such attacks is challenging, given the constantly evolving range of attack techniques. In this paper, a novel hybrid approach is proposed that combines statistical methods with machine-learning capabilities to address the detection and mitigation of DDoS attacks in SDN environments. The statistical phase of the approach utilizes an entropy-based detection mechanism, while the machine-learning phase employs a clustering mechanism to analyze the impact of active users on the entropy of the system. The k-means algorithm is used for clustering. The proposed approach was experimentally evaluated using three modern datasets, namely, CIC-IDS2017, CSE-CIC-2018, and CICIDS2019. The results demonstrate the effectiveness of the system in detecting and blocking sudden and rapid attacks, highlighting the potential of the proposed approach to significantly enhance security against DDoS attacks in SDN environments. [ABSTRACT FROM AUTHOR]

In recent times, Software Defined Networking (SDN) has gained immense popularity among communication networks due to its high network connectivity and security. But, owing to the lack of sufficient security schemes and dynamic access control, conventional techniques could be more efficient. Hence, this paper proposes a secure intrusion detection scheme for SDN utilizing LogishFTS-based Recurrent Neural Networks (LFTS-RNN) and Poisson Clumping Japanese Tree Frog Optimization Algorithm (PC-JTFOA). Initially, by using a username, password, and Signed Access Control (SAC), the registered users log in to the network. The Entropy Makwa-based Digital Signature Algorithm (EMDSA) is utilized to identify the attackers during the login process. Then, the Intrusion Detection System (IDS) is trained using the historical dataset. The critical features are extracted in IDS; subsequently, by using the PC-JTFOA, the optimal features are selected. Likewise, the selected features are inputted into the LFTS-RNN to recognize the intruders in the application plane. Moreover, the process of data security and load balancing is undergone by the data. Afterward, the balanced data is subjected to IDS to detect the attackers in the control plane. According to experimental outcomes, the proposed technique obtained a higher prominence with an accuracy of 98.35%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the Lion Optimization Algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings. [ABSTRACT FROM AUTHOR]

The social internet of vehicle (SIoV) is a specialized network combining intelligent sensing devices and vehicular communications to address traffic monitoring and resource management challenges in smart cities. Ensuring efficient and sustainable green computing with global network stability is crucial, especially in the dynamic environment of vehicular mobility. The software-defined-SIoV (SD-SIoV) architecture separates control and forwarding planes for centralized management. The architecture addresses green traffic data dissemination with heterogeneous traffic data by formulating control plane nodes' election as an NP-Hard optimization problem, considering parameters, e.g., transmission distance, node's residual energy, load imbalance, and mobility factor. The architecture incorporates the random way-point mobility (RWPM) model for simulating nodes' mobility. The proposed improved energy-efficient gray wolf optimization (IEEGWO) algorithm enhances energy-efficiency by intelligently electing and re-electing optimal control plane nodes, jointly addressing load imbalance and fault-tolerance issues, ultimately improving green computing and communication performance in SD-SIoV. Comparative analysis with state-of-the-art demonstrates that IEEGWO provides significant green computing benefits in a real-time SIoV scenario [ABSTRACT FROM AUTHOR]

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic. [ABSTRACT FROM AUTHOR]

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network's matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement. [ABSTRACT FROM AUTHOR]

The advancement of technology allows for easy adaptability with IoT devices. Internet of Things (IoT) devices can interact without human intervention, which leads to the creation of smart cities. Nevertheless, security concerns persist within IoT networks. To address this, Software Defined Networking (SDN) has been introduced as a centrally controlled network that can solve security issues in IoT devices. Although there is a security concern with integrating SDN and IoT, it specifically targets Distributed Denial of Service (DDoS) attacks. These attacks focus on the network controller since it is centrally controlled. Real-time, high-performance, and precise solutions are necessary to tackle this issue effectively. In recent years, there has been a growing interest in using intelligent deep learning techniques in Network Intrusion Detection Systems (NIDS) through a Software-Defined IoT network (SDN-IoT). The concept of a Wireless Network Intrusion Detection System (WNIDS) aims to create an SDN controller that efficiently monitors and manages smart IoT devices. The proposed WNIDS method analyzes the CSE-CIC-IDS2018 and SDN-IoT datasets to detect and categorize intrusions or attacks in the SDN-IoT network. Implementing a deep learning method called Bidirectional LSTM (BiLSTM)--based WNIDS model effectively detects intrusions in the SDN-IoT network. This model has achieved impressive accuracy rates of 99.97% and 99.96% for binary and multi-class classification using the CSE-CIC-IDS2018 dataset. Similarly, with the SDN-IoT dataset, the model has achieved 95.13% accuracy for binary classification and 92.90% accuracy for multi-class classification, showing superior performance in both datasets. [ABSTRACT FROM AUTHOR]

Summary: In this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes. [ABSTRACT FROM AUTHOR]