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In the software defined network (SDN)/network function virtualization (NFV)-enabled networks, service function chains (SFCs) should typically be allocated to deploy these services, which not only entails meeting the service’s Quality of Service (QoS) requirements, but also considering the infrastructure’s limitations. Although this issue has received much attention in the literature, the dynamics, intricacy, complexity and unpredictability of the issue provide several difficulties for researchers and engineers. The traditional methods (e.g., exact, heuristic, meta-heuristic, and game, etc.) are subjected to the complexity of multi-domain cloud network scenarios with dynamic network states, high-speed computational requirements, and enormous service requests. Recent studies have shown that reinforcement learning (RL) is a promising way to deal with the limitations of the traditional methods. On-policy and off-policy are two key categories in the field of RL models, and they both have promising advantages in deal with dynamic resource allocation problems. This paper contains two innovative points at two levels. Firstly, in order to deal with SFC embedding problem in dynamic multi-domain networks, a mixed Markov model combining Markov decision process (MDP) and hidden Markov model (HMM) is constructed, and the corresponding RL model-solving algorithms are proposed. Secondly, in order to distinguish the appropriate model in a given network scenario, the on-policy RL based multiple domain SFC embedding algorithm is compared with the off-policy one. The obtained simulation results show that the proposed RL algorithms can outperform the current baselines in terms of delay, load balancing and response time. Furthermore, we also point out that the off-policy based algorithm is more suitable for small-scale dynamic network scenarios, while the on-policy based algorithm is more suitable for medium to large-scale network scenarios with high convergence requirements.

The proliferation of Internet of Things (IoT) devices and other IT forms in almost every area of human existence has resulted in an enormous influx of data that must be managed and stored. One viable solution to this issue is to store and handle massive amounts of data in cloud environments. Real-time data analysis has always been critical. However, it becomes even more crucial as technology and the IoT develop, and new applications emerge, such as autonomous cars, smart cities, and IoT devices for healthcare, agriculture, and other industries. Given the massive volume of data, moving to a remote cloud is time-consuming and produces severe network congestion, rendering cloud administration and rapid data processing difficult. Fog computing provides close-to-device processing at the network’s periphery, and fog computing can analyze data in near real-time. However, the increased amount of IoT gadgets and data they produce is a formidable challenge for fog nodes. Task offloading may enhance fog computing by offloading the excess data to other nodes for processing due to the restricted resources in the fog. Management of tasks and resources must be optimized in fog devices. This review article overviews related works on task offloading in IoT-Fog-Cloud Environment. In addition, we discuss about fog networks and Software-defined network (SDN) applications and challenges in fog offloading.

This state-of-the-art review comprehensively examines the landscape of Distributed Denial of Service (DDoS) anomaly detection in Software Defined Networks (SDNs) through the lens of advanced Machine Learning (ML) and Deep Learning (DL) techniques. The application domain of this work is focused on addressing the inherent security vulnerabilities of SDN environments and developing an automated system for detecting and mitigating network attacks. The problem focused on in this review is the need for effective defensive mechanisms and detection methodologies to address these vulnerabilities. Conventional network measurement methodologies are limited in the context of SDNs, and the proposed ML and DL techniques aim to overcome these limitations by providing more accurate and efficient detection and mitigation of DDoS attacks. The objective of this work is to provide a comprehensive review of related works in the field of SDN anomaly detection recent advances, categorized into two groups via ML and DL techniques. The proposed systems utilize a variety of techniques, including Supervised Learning (SL), Unsupervised Learning (UL) Ensemble Learning (EL) and DL solutions, to process IP flows, profile network traffic, and identify attacks. The output comprises the mitigation policies learned by ML/DL techniques, and the proposed systems act as sophisticated gatekeepers, applying automated mitigation policies to curtail the extent of damage resulting from these attacks. The results obtained from the evaluation metrics, including accuracy, precision, and recall, confirm the marked effectiveness of the proposed systems in detecting and mitigating various types of attacks, including Distributed Denial of Service (DDoS) attacks. The proposed systems’ foundational contributions are manifest in their efficacy for both DDoS attack detection and defense within the SDN environment. However, the review acknowledges certain inherent limitations and the pressing need for further validation within real-world scenarios to assess the proposed methods’ practicality and effectiveness. In summary, this systematic review offers valuable perspectives on the present status of Distributed Denial-of-Service detection in Software-Defined Networks employing Machine Learning and Deep Learning methodologies, highlighting the strengths and limitations of various proposed systems and identifying areas for future research and development.

In order to solve the problems of unreasonable traffic path allocation, elephant flow collision and high overhead of controller flow table in data center networks, this paper proposed an SDN based hybrid segment routing probability flow scheduling mechanism (SRPFS). By taking advantage of SDN s centralized control feature, SRPFS initialized the traffic forwarding method by hybrid segment routing at first. Then, it utilized particle swarm optimization and redefined the internal particles optimization process to filter the traffic. Finally, SRPFS constructed the global node probability matrix and designed probability scheduling algorithm to elect the optimal path for traffic forwarding. Experimental results show that the hybrid segment routing has a greater advantage in terms of flow table overhead. Compare with other classic flow transmission mechanisms, SRPFS has certain advantages in average network throughput, link utilization and standard network throughput rate, it reduces the controller flow-table overhead and ensures better network performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The security flaws in cyber security have always put the users and organizations at risk, which as a result created catastrophic conditions in the network that could be either irreversible or sometimes too costly to recover. In order to detect these attacks, Intrusion Detection Systems (IDSs) were born to alert the network in case of any intrusions. Machine Learning (ML) and more prominently deep learning methods can be able to improve the performance of IDSs. This article focuses on IDS approaches whose functionalities rely on deep learning models to deal with the security issue in Internet of Things (IoT), wireless networks, Software Defined Networks (SDNs), and Industrial Control Systems (ICSs). To this, we examine each approach and provide a comprehensive comparison and discuss the main features and evaluation methods as well as IDS techniques that are applied along with deep learning models. Finally, we will provide a conclusion of what future studies are possibly going to focus on in regards to IDS, particularly when using deep learning models.

The MQTT (Message Queue Telemetry Transport) protocol has recently been standardized to provide a lightweight open messaging service over low-bandwidth and resource-constrained communication environments. Hence, it is the primary messaging protocol used by Internet of Things (IoT) devices to disseminate telemetry data in a machine-to-machine approach. Despite its advantages in providing reliable, scalable, and timely delivery, the MQTT protocol is widely vulnerable to flooding and denial of service attacks, specifically, the low-rate distributed denial of services (LR-DDoS). Unlike conventional DDoS, the LR-DDoS attack tends to appear as normal traffic at a very slow rate, which makes it difficult to differentiate from legitimate packets, allowing the packets to move undetected by traditional detection policies. This paper presents an intelligent lightweight detection scheme that can capture LR-DDoS attacks based on MQTT protocol in a software-defined IoT environment. The proposed scheme examines the performance of four machine learning models on a modern dataset (LRDDoS-MQTT-2022) with a minimum feature set (i.e., two features only) and a balanced dataset, namely: decision tree classifier (DTC), multilayer perceptron (MLP), artificial neural networks (ANN), and naïve Bayes classifier (NBC). Our exploratory assessment demonstrates the arrogance of the DTC detection scheme achieving an accuracy of 99.5% with peak detection speed. Eventually, our best outcomes outdo existing models with higher prediction rates.

With increased importance of mobile networks and their expected ability to be usercentric puts strain on the current mobile networks. To tackle this, 5G networks will be used which offers user-oriented operation at effective costs with excellent infrastructure capabilities which can deal with heavy network traffic. Along with this, 5G networks will be useful in varied fields - from business use cases to medical ones. SDN technology has certain components which can be integrated with the 5G network following proper analysis and after realizing its proper applicability. The 5G network coupled with SDN will bring out some outstanding innovations in the network and its infrastructure. Leveraging SDN and NFV architecture as well as technology to build effective 5G networks is highlighted in this paper. SDN simplifies the network complexity because of its existing framework which suits the network framework in discussion. This paper summarizes the different approaches taken to achieve the above said aim. The primary focus remains using SDN for 5G networks and utilizing related technologies to get the best result. [ABSTRACT FROM AUTHOR]

The security flaws in cyber security have always put the users and organizations at risk, which as a result created catastrophic conditions in the network that could be either irreversible or sometimes too costly to recover. In order to detect these attacks, Intrusion Detection Systems (IDSs) were born to alert the network in case of any intrusions. Machine Learning (ML) and more prominently deep learning methods can be able to improve the performance of IDSs. This article focuses on IDS approaches whose functionalities rely on deep learning models to deal with the security issue in Internet of Things (IoT), wireless networks, Software Defined Networks (SDNs), and Industrial Control Systems (ICSs). To this, we examine each approach and provide a comprehensive comparison and discuss the main features and evaluation methods as well as IDS techniques that are applied along with deep learning models. Finally, we will provide a conclusion of what future studies are possibly going to focus on in regards to IDS, particularly when using deep learning models. [ABSTRACT FROM AUTHOR]

With the increase in the number of vehicles and intelligent applications in the IoV, computing intensive tasks have proliferated, and the traditional architecture is difficult to meet user needs. To solve the problems of insufficient and uneven allocation of computing resources in the IoV, insatiable application delay requirements, and high task energy consumption costs, this paper combined MEC and SDN to design an efficient task offloading scheme in the network of vehicles architecture from macro station to MEC server to vehicle, and proposed an improved low-complexity non-dominated sorting genetic algorithm to optimize the task offloading cost and the load balancing rate of MEC server. The experimental simulation results show that the proposed scheme compared with random offloading, NO-MEC offloading, NO-I offloading, traditional NSGA,NSGA-Ⅱ off loading, GA offloading, Q-learning offloading, DQN offloading, has lower offloading cost, better load balancing rate, and approximately the highest system utility, which brings better network services to vehicle users in the IoV. [ABSTRACT FROM AUTHOR]

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL-KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid growth of multimedia services, the burden of infrastructure is becoming heavier, which may not guarantee a high-quality experience for users. Whether the multimedia category is video on demand (VOD), live broadcast, or social media is a hot issue. Traditionally, content delivery network (CDN) service providers deploy servers as close to clients as possible to reduce latency. CDN is an overlay network mainly responsible for routing requests, distribution, delivery, and audit. As a supplement to computing and storage capacity, CDN service providers have begun migrating some of their services to the cloud to focus on the delivery process. At the same time, the collaboration between CDN service providers promotes scalability to meet the growing number of requests. However, the underlying physical layer needs to be improved. The cost of the traditional underlying network is very high and cannot be easily expanded. The concept of software definition network (SDN) is proposed to solve these problems. SDN functions are divided into control layer and data layer. In the SDN, the global view is achieved to receive information by centralization, and then the deployment is dynamically adjusted to control the network. In this paper, we summarize the advantage structure and problems of a newly developed system SDCDN (software defined content distributed network). We introduce similar research progress. Then we summarize the current situation of SDN and CDN, compare and analyze their advantages and disadvantages, and finally put forward the directions of open issues and the future research.

The advent of Intelligent Transport Systems (ITS) has led to the appearance of vehicles increasingly connected to their environment on global road networks. Due to the strict requirements for low latency and secure interactions in a vehicular environment, the proposal of new architectures is a crucial topic for discussion. This paper aims to develop a vehicular network using several access technologies based on SDN (Software Defined Network) paradigm, to take advantage of the capacities of the various access networks and provide flexibility in their control and management. Confidentiality, integrity, and authentication are essential services to prevent an adversary from compromising the security of vehicular networks. Therefore, good security and privacy management system is necessary to ensure this protection. We represent then a hybrid SDN-VANET architecture that can address all of the challenges we mentioned earlier. We are in the process of implementing a dynamic approach to optimize the positioning of controllers according to changes in network topology due to fluctuations in road traffic. We will also detail the topology estimation service based on machine learning techniques to provide network control functions with potential insight into the future state of the network, unlocking proactive and intelligent network control. We also provide a scheme that prevents and informs about basic and compound attacks and reacts to the privacy and security conditions of the vehicular network, managing the requirements of security management systems. The simulation results showed the effectiveness of the proposed schemes in terms of message loss rates, packet delivery rates (PDR), Round Trip Time (RTT), and delays. With used our scheme, the performance of the network is improved when SDN triggers the change of the RSU entity. Such as we notice that the average RTT is lowered by 68 ms and that the PDR remains around 94%. We also notice with the integration of the security and privacy scheme (SPS) that the performance of the network is improved, the average RTT is reduced by 51 ms and the PDR persists around 99%.

In current years, increased number of cyberspace users cause rapid ascends of network traffics. For instance: probability of receiving network traffic ever since software technologies that linked with devices produced massive amounts of data which are unable to accommodate through conventional schemes port based, payload based and machine learning approaches. Simultaneously SDN technology can alleviate problems of conventional method in classifying network traffic as malicious and benign, resources allocation, network monitoring along with enhancement in overall network performance via activist methods. This research work analyzed the net traffic metadata of 1,04,345 samples gathered from RYU-SDN controller, an OpenFlow controller using mininet emulator with 23 features then performed encrypted metadata categorization into three classes namely TCP, UDP and ICMP attacks through deep CNN with two layers LSTM, CNN-two layers GRU and ConvNet Bidirectional with two layers GRU approaches with hyper parameters tuning appropriate for better network convergence, performance, optimization too. The proposed experimental outcomes reveals that deep based CB-GRU method fulfill traffic classification in SDN environment and accomplished significance enhancement in terms of accuracy 99.97%, and loss rate 0.01. Other evaluation criterias precision, recall, area under curve, were calculated for performance identification in net data traffic classification than conventional methods. [ABSTRACT FROM AUTHOR]

Currently, there is an enormous disturbance regarding privacy in information and communication technology around the scientific community. Since any assault or abnormality in the network can seriously disturb numerous realms like national security, private data storage, social welfare, economic issues, and so on. Consequently, one of the domains for detecting intrusion in the network is anomaly detection domain and it is a wide probe area. Various numerous methods and approaches have developed for anomaly detection. In the network security field, traffic anomaly detection has been a main aspect. The network security domain recognizes assaults in terms of significant deviations from the entrenched regular usage profiles. Nowadays, software-defined networking (SDN) is a new networking model has developed to ease effectual network control and management. This view investigates 50 probe papers focused on traffic flow rate prediction-based anomaly detection in SDN. Furthermore, it presents technique wise classifications like flow counting-based techniques, information theory-based approaches, entropy-based techniques, deep learning (DL)-based approaches, hybrid methods and network methods. An examination includes in an overview based on classification research techniques, toolset used, years of publication, datasets, and evaluation metrics for predicting anomaly in the SDN environment. Lastly, the limitations of surveyed techniques are explained, that encourage investigators for inventing more new techniques for predicting anomaly in SDN. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high routing algorithm complexity, low QoS flow satisfaction and single link failure in current software-defined network (SDN) architecture, a multi-constraint QoS dual-path routing optimization algorithm based on software-defined network (SDN_ MCQDP) is proposed. The controller is used to obtain the global network state information, and generate a directed acyclic graph based on the destination node. In the multi-constraint QoS routing stage, the multi-constraint problem is transformed into a linear programming problem by the Lagrangian relaxation dual algorithm. The reverse link is used to delete redundant dual-path links that meet multi-constraint QoS and ensure data transmission after link failure. The algorithm is simulated and analyzed from the aspects of routing calculation time, link utilization, and QoS flow satisfaction. The results show that, compared with MODLARAC, QT, RMCDP_RD, and H_MCOP algorithms, SDN_MCQDP can effectively reduce the transmission delay and route calculation time, improve the link utilization, and still meet the QoS requirements after link failure. [ABSTRACT FROM AUTHOR]

The biggest challenge for the network service providers is the day to day advancement of technologies which makes them difficult to manage the traditional networks. This day to day advancement has worked as a motivation to vendors for developing, deploying and migrating their services, installments of new hardware, trained people and up gradation of infrastructure which involves a huge cost and time. These frequent changes demand a new network architecture which supports future technologies and solves all these issues named as the proposal of networks defined by software. A large amount of data is being generated and through the internet, we interact with the world using our smart devices such as tablets, sensors, and smartphones using the concepts of Internet of Things (IoT). Along with continuous growth and development, there is a continuous heterogenous and ever-increasing demands of services. This leads to a cause of emerging challenge of load balancing of networks for meeting up with highly demanding requirements (e.g., high performance, lower latency, high throughput, and high availability) of IoT and 5G network applications. For meeting up highly increasing demands, various proposal of load balancing techniques comes forward, in which highly dedicated balancers of loads are being required for ever service in some of them, or for every new service, manual recognition of device is required. In the conventional network, on the basis of the local information in the network, load balancing is being established. However, the production of more optimized load balancers and a global view for the network is being contained by SDN controllers. So, these well-known techniques are quite time-consuming, expensive and impractical as well as service types aren't being considered by various existing load balancing schemes. Through this paper, researchers focus on an SDN based load balancing (SBLB) service, in which minimized response time and maximized resource utilization are being considered for the user on cloud servers. The proposed scheme is being constituted by an application module which runs along with a SDN controller and server pools that connect to the controller through SDN enabled switches. The application module contains a dynamic load balancing module, a monitoring module and a service classification module. All messages are being handled in real time and host pool are being maintained by the Controller. The performance of the proposed scheme has been validated by experimental results. Through various experiments, results are being concluded that usage of SBLB results in significant decrease in average response and reply time. [ABSTRACT FROM AUTHOR]

Nowadays, a massive amount of data leads to cause network traffic and inflexible mobility in future mobile networks. A new Group Mobility Model (GMM) named MoMo is introduced that addresses the issue of the aforementioned problems. Even though, software defined network (SDN) is functional with network-rooted mobility protocols that enhance the network efficiency. Some existing network-rooted mobility administration methods still undergo handover delay, packet loss, and high signaling cost through handover processing. In this research work, SDN-based fast handover for GMM is proposed. Here, the neighbor number of evolving node transition probabilities of the mobile node (MN) and their obtainable resource probabilities are estimated. This makes a mathematical framework to decide the preeminent number of the evolving nodes and then allot these to mobile nodes virtually with all associations finished by the exploit of Open-Flow tables. The performance examination demonstrates that the proposed SDN rooted GMM technique has the enhanced performance than the conventional handover process and further technique by handover latency, signaling cost, network throughput, and packet loss. [ABSTRACT FROM AUTHOR]

Summary: Extreme densities are an important factor in future mobile networks that can provide very high data rate to mobile users. It also increases complexity in handover decisions and resource management. However, SDNs (software‐defined networks) and mobility models can provide seamless mobility and efficient resource management in heterogeneous mobile environment, and ensure QoS (quality of service) is achieved. The paper proposes SDN based on GMM (Group Mobility Model) called MoMo, and resource management using MLOAs (mutation lion optimization algorithms) are used to alleviate handover and addressing issues in network congestion. The proposed method is based on SDN controllers working in global perspectives for achieving required network conditions and end user QoSs. SDN with GMM‐MoMo‐MLOA offer transparent and dynamic support for sessions during handoffs and thus eliminates congestion overheads and packet losses related to mobile traffic, resulting in improved QoS for mobile users. The performance analysis through experiments depicts that the proposed model gives enhanced performance when compared to that of the other conventional methods and also has proven efficiency in terms of other parameters such as handover latency, signaling cost, throughput, and packet loss. The results from the simulation show that the proposed method SDN‐GMM‐MoMo‐MLOA greatly improves the performance of network and also maintains optimum resource utilization and efficiency. [ABSTRACT FROM AUTHOR]

The flow fluctuations in the highly dynamic Internet of Vehicles (IoV) make the IoV difficult to provide reliable and scalable wireless network services for the emerging applications in the 5 G and beyond era. The software-defined networks (SDN) could feasibly manage and optimize the network according to the network load. Controller placement is a critical problem in SDN to achieve its robustness and flexibility with the changes of network status. Motivated by the advantages of SDN and Mobile-edge computing (MEC), this paper aims at enhancing the performance of IoV with the assistance of these two. Specifically, we consider a three-layer hierarchical SDN control plane for the IoV, where the SDN controllers are placed at the edge of networks. Under this framework, we investigate a multi-objective optimization problem on controller placement problem including delay, load balancing, and path reliability. To efficiently solve the formulated NP-hard problems, we develop an algorithm based on multi-agent deep Q-learning networks (MADQN) because of its advantages for large-scale combinatorial optimization. At last, we use multi-process technology to accelerate the operation of the algorithm, so as to complete the algorithm iteration faster. Numerical results show that the proposed methods achieve better performances than three baselines. [ABSTRACT FROM AUTHOR]

Intelligent Transport Systems (ITSs) play an important role in future smart city design to improve traffic safety and traffic congestion by sharing data collected by vehicles. For sharing the traffic data with other vehicles, the vehicular sensory data are usually uploaded to the cloud server. However, existing data sharing systems for VANETs cannot provide selective data with sufficient privacy protection. Moreover, some schemes also cannot ensure stable data accessibility and the integrity of retrieved data. On the other hand, with the improvements such as lower latency, higher capacity, and increased bandwidth, 5G technology brings more possibilities to future applications. The join of the software-defined networks (SDNs) also offers efficient and effective network management. This paper proposes a primitive vehicular communication system named blockchain-based privacy-preserving and sustainable data query service. The proposed scheme is designed to realize stable data accessibility by leveraging smart contracts and blockchain oracle. With the help of 5G technology and P2P file-sharing system, InterPlanetary File System (IPFS), the proposed scheme aims to support video downloading files with searchable capability and fairness. An incentive token mechanism is also equipped. The merit of auditability is ensured by Ethereum blockchain platform to support the accountability. Besides, we also evaluate its networking performance via SUMO and NS-3 simulators. Our simulation results show that the request-response delay of BPSDQS is less than existing blockchain-based proxy re-encryption (PRE) scheme. Our simulation results also showed that the average request-response delay in our scheme can saving up to 98%. [ABSTRACT FROM AUTHOR]

Energy Internet (EI) is developing and booming rapidly with the increase of distributed energy resources, which is beneficial to address the severe condition of industrial energy. However, there are inevitable credit crises and utility optimization challenges in EI that need to be settled. In this article, we propose a blockchain-assisted software defined energy Internet (BSDEI), where a distributed energy market smart contract is designed to ensure transactions executed reliably and participants’ accounts dealt accurately. In order to jointly optimize the utilities of operators, retailers, and industrial prosumers in BSDEI, we formulate the whole trading process as a three-stage Stackelberg game, with the proof of existence and uniqueness for the Stackelberg equilibrium. Then, we design a hierarchical distributed policy gradient algorithm to solve the Stackelberg game under incomplete information. We implement a blockchain-based industrial energy trading system using a middleware platform. The smart contract is deployed on the consortium blockchain, providing website interfaces for participants to operate. Furthermore, we conduct experiments for analyzing economic benefits. Our system prototype demonstrates the feasibility of BSDEI and the algorithm exceeds about 18% in total mean reward than comparing algorithms. [ABSTRACT FROM AUTHOR]

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics. [ABSTRACT FROM AUTHOR]

A Data Center Network (DCN) is composed of a large number of computing and storage nodes that are interconnected by well-organized switches. The Software-Defined Networking (SDN) based DCN (SD-DCN) improves resource utilization and provides virtual network access by separating the data plane and control plane of DCN. However, the routing strategies in current SD-DCN systems are based on traditional mechanisms that lack in real-time modification and are less efficient in resource utilization. To overcome these limitations, Convolutional Neural Network (CNN) deep learning model is proposed in this paper to improve the routing computation in SD-DCN, i.e., FAT-tree topology. The CNN deep learning model gives intelligent paths according to online training of traffic patterns. Moreover, the achieved network performance is compared with specific existing routing algorithms for SD-DCN. It is observed that the average network throughput is almost doubled for hot-spot traffic as compared with existing routing algorithms OSPF and FlowDCN. The experimental results show that, compared to ANN, the proposed model has increased the average network throughput by approximately 40%. Also, the proposed CNN model has outperformed the Artificial Neural Network (ANN) model in terms of average network delay and packet loss rate. Similarly, the overall bandwidth utilization is achieved by approximately 70% as compared to existing mechanisms. [ABSTRACT FROM AUTHOR]

The distributed denial of service ( DDoS) attack detection method in traditional software defined network ( SDN ) requires frequent communication between the control plane and the data plane, which will lead to significant overhead and delay, and the current programmable data plane can not implement complex detection algorithms, so it is difficult to ensure high detection efficiency. To solve the above problems, this paper proposed a DDoS attack detection method based on programming protocol-independent packet processors ( P4 ) programmable data plane. First of all, the method used the improved information entropy based on P4 as initial detection to determine whether suspicious traffic occurred . Then, it took advantage of the microsecond time required for feature extraction by P4 to extract the six-tuple features of suspicious traffic, and imported them into the data standardization-deep neural network ( DS-DNN ) reinspection module to determine whether they were DDoS attack traffic . Finally, it tested the evaluation indicators of the method in the real environment. The experimental results show that this method can better detect DDoS attacks in SDN environments, ensure high detection rate and accuracy, effectively reduce the false alarm rate, and shorten the detection time to millisecond level. [ABSTRACT FROM AUTHOR]

The distributed denial of service (DDoS) vulnerabilities have rapidly extended and have been given different possibilities for even more advanced assaults on specific targets in recent times, thanks to the growth of innovative technology such as the Internet of Things (IoT) and Software-Defined Networking (SDN). The attack patterns route comprises unprotected and susceptible IoT systems that are internet-connected, as well as denial of service weaknesses in the SDN controllers, such as southbound connection exhaustion. (1) Background: The review does not go into detail about the symmetry blockchain approaches used to mitigate DDoS attacks, nor does it classify them in IoT; (2) To overcome the privacy issues, a novel deep learning-based privacy preservation method was proposed named ShChain_3D-ResNet. This novel method combines Sharding, blockchain and Residual Network for securing the SDN. Under this network, the proposed efficient attention module jointly learns attention to enforce the symmetry on weights for various channels in spatial dimension as well as attention weights of multiple frames in temporal dimension assistance of pre-training, updating, and dense convolution process; (3) Results: the proposed ShChain_3D-ResNet achieves 95.6% of accuracy, 97.3% of precision, 95.2% of recall, 94.4% of F1-score, 32.5 ms of encryption time and 35.2 ms of decryption time for dataset-1. Further, it achieves 97.3% accuracy, 95.3% precision, 96.1% recall, 98.2% F1-score, 32.1 ms of encryption time, and 36.2 ms of decryption time for dataset-2; (4) Conclusions: The Sharding strategy can increase ShChain performance while simultaneously utilizing Multi User (MU) resources for SDN. [ABSTRACT FROM AUTHOR]

One goal of the sixth generation (6 G) is to extend the communication abilities of a Gbps bitrate, low latency and high reliability to global areas. The Space-Air-Ground Network (SAGN) is a promising scheme. Deterministic services in SAGN are very important for network providers, but service conflicts and a lack of end-to-end feature abstractions restrict the development of more services and applications. Abstracting the network features to design service components and abilities is the key issue. Therefore, this paper proposes a new service development scheme for SAGNs, which provides global service components and abilities based on a microservice framework for different networks. We explore the unified feature description method based on the ground state, which decouples the network element function (NEF) from different end-to-end networks. A convex optimisation model based on reversible driving factors is designed for the service developments model, which can optimise the choreographies and combinations of the services in SAGN. A feature compression method based on equivalent mapping service description and orbital shrinking is proposed to improve the development efficiency of SAGN services. Simulations and tests are conducted to examine the performance enhancement of services, which improves the service generating speed by 14.5% and the service conflict rate by 23.4%. [ABSTRACT FROM AUTHOR]

The development of information technology and computer network from time to time is increasing along with the increase in user needs for both from the business, education, industrial, to data security side. Data of network traffic that is getting denser in communication and data exchange between users on computer networks can become a problem when using conventional computer network technology. For that, it needs a new technology that is implemented in computer networks, along with the measurement of Quality of Service (QoS) in it. Software-Defined Networking (SDN) is a solution for this, where the stages of network design, management and implementation, separate the data plane and the control plane. In this research, the implementation of SDN was carried out in the form of a simulation using both of Mininet and OpenDaylight with a Tree Topology, then the QoS measurements were carried out in it. The results of testing and measuring QoS on SDN simulations with Tree topology using Mininet and OpenDaylight, showed a Jitter value of 0.425 ms, a Packet Loss value of 0.266%, a Bandwith value of 9.3925 Mbps, a UDP Throughput value of 2.348 bits/sec, and a TCPThroughput value of 2.335 bits/sec.

Wireless Sensor Networks (WSNs) is an emerging field that enables them to work in complex scenarios with enough flexibility. The WSN systems are improved in terms of network flexibility by integrating Software Defined Network (SDN) to obtain SDWSN environment. However, security and energy efficiency are the major issues in the SDWSN environment which leads to low network lifetime. To resolve the existing issues, we proposed DAG-based BLOCKchain-Routing and Load Balancing Model (DBLOCK-RLB). The proposed approach performs routing and load balancing in the SDWSN environment in an energy efficient manner with high security. The SDWSN environment is composed of four major planes as data plane, switch plane, control plane, and application plane. The sensor nodes in the data plane are authenticated priory to ensure legitimacy and reduce unwanted network traffics by adopting Camellia Encryption Algorithm (CAE) which provides secret key to the user by considering several metrics. The authenticated sensor nodes are clustered to reduce energy consumption using Adaptive Threshold based Network Partitioning in which the circular SDWSN environment is split into equal parts and optimal Cluster Head selection is done by considering clustering metrics. From the clustered network, the CH is responsible for intelligent routing. For intelligent routing, we utilize Dual Agent-Twin Delayed Deep Deterministic Policy Gradient (DA-TD3) in which one agent for optimal forwarder selection and another agent for optimal route selection. After routing, the load balancing is done to reduce the network overhead and scalability issues. The proposed work performs trading based load balancing by utilizing Stackelberg Game Model in which all the local controllers are considered as followers and global controller is considered as leaders. All the transactions are stored in the DAG blockchain to reduce the scalability and Proof-of Authentication (PoAH) used to verify the transactions. The proposed work is simulated using NS-3.26 simulation tool and compared with several existing works by validation metrics like throughput at 2.55(mbps), latency at 27.1(ms), packet delivery ratio at 91.5(%), network Lifetime at 546.5(s), and energy consumption at 14.5 (J). The results show that the proposed work outperforms better than the existing works. [ABSTRACT FROM AUTHOR]

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