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The Internet of Things (IoT) allows for the coordination and orchestration of a large number of physical and virtual objects connected to the Internet. Sensors used in IoT applications may be in remote locations with an unstable network infrastructure or with limited management of their functions due to processing overload on devices responsible for performing their management (i.e. IoT Gateways) through IoT services. A failure or overload on a network link associated with an IoT Gateway, or failure on the IoT Gateway itself due to processing overload, may result in temporary or even permanent unavailability in IoT applications (Web, Mobile, and Desktop). To address these problems, this paper presents a solution for the need to execute load balancing for the Fog of Things Platforms (in the Fog, and between Fog and Cloud) via the programmability through the use of Software-Defined Networks (SDN). The IoT environment proposed in this work is based on the Fog of Things (FoT) paradigm, which proposes the provision of processing, storage and IoT services at edge devices (local network), optimizing , when possible, the use of IoT services in the Cloud.

Software Defined Networking is an evolving network model wherein the control plane is decoupled from data plane. It has become a fascinating problem to decide the number of controllers and their positions, and to allocate switches to them. Each switch must be assigned to a backup controller so that if a controller encounters failure then the switches which are assigned to it can be immediately connected to their backup controllers. An existing method attempts to solve this problem by employing mixed integer linear programming; but it suffers from huge increase in execution time for larger networks. In order to reduce the execution time, this paper proposes a simulated annealing-based heuristic which aims to minimize the maximum of latencies from all switches to the respective backup controllers. The proposed algorithm is evaluated on seven real networks of varying sizes from Internet Topology Zoo and its performance is compared with the existing model. The results show that the proposed model achieves an average speed-up of 2.5 over the existing model (for the smallest network) and an average speed-up of 280 over the existing model (for the largest network). And at the same time, the proposed model produces near optimal solution.

The Software Defined Networking (SDN) paradigm decouples the network control functions from the data plane and offers a set of software components for flexible and controlled management of networks. SDN has promised to provide numerous benefits in terms of on-demand provisioning, automated load balancing, streamlining physical infrastructure, and flexibility in scaling network resources. In order to realize these network service offerings, there is an important need for developing an efficient, robust, and secure execution platform. As a primary contribution, we present a novel virtual execution platform for the OpenFlow controller using Network Function Virtualization (NFV). Theoretically, NFV can apply to any network function, which can simplify the managing of the heterogeneous data plane. The characteristics of our proposed architecture include pipe-lined processing of network traffic, virtualized and replicated execution of network functions, isolation between task nodes, and random mapping of traffic to task nodes. The proposed architecture has two major components: a Network Packet Schedulers (NPS) and a Task Engine (TE). The TE consists of Task Nodes (TNs) which are responsible for executing different network functions on various traffic flows and each TN is realized as a virtual machine. Upon receiving traffic from the data plane, NPS analyses the functional requirements of the traffic and different controller performance parameters. Then it allocates the traffic to appropriate TNs for executing necessary network functions. In this respect, it provides performance benefits, robustness, fine-grained modularity, and strong isolation security in the processing of traffic flows on the SDN platform. Efficacy of our proposed architecture has been demonstrated with a case study.

The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

Fog computing (FC) is a contemporary computing paradigm that gives additional support to cloud environment by carrying out some local data analysis in edge of the devices, facilitating networking, computing, infrastructure and storage support as backbone for end user computing. Still enterprises are not convinced to use this as security and privacy are most of the open and challenging issues. Availability among the security requirements is the one which is about rendering on demand service to different client applications without any disruptions. It can often be demolished by Denial of service (DoS) and distributed denial of service (DDoS) attacks in fog and cloud computing environment. In this paper we propose a novel Source based DDoS defence mechanism which can be used in fog environment as well as the cloud environment to mitigate DDoS attacks. It makes use of Software Defined Network (SDN) to deploy the DDoS defender module at SDN controller to detect the anomalous behavior of DDoS attacks in Network/Transport level. The proposed work provides deep learning (DL) based detection method which makes use of the network traffic analysis mechanisms to filter and forward the legitimate packets to the server and can block the infected packets to cause further attacks.

Software-Defined Networking (SDN) is a modern paradigm that provides a platform for implementing reliable, centrally managed, and automated security solutions for conventional and new generation networks, such as IoT, cloud computing, 5G/6G mobile communication networks, and vehicular communications. In these complex systems, manual security operations can delay or obstruct the identification, mitigation, and prevention of ever-increasing sophisticated threats. Thus, the idea of security automation for networks using the SDN paradigm has become fundamental, given that SDN was created to facilitate the operation and management of complex networks with minimal human intervention, which is considered error-prone. This survey studies the state-of-the-art research efforts concerned with security automation in SDN environments. We identified and ranked various classes of security solutions with different levels of automation and complexity. The level of automation is measured using four well-defined qualitative parameters: self-healing, self-adaptation, self-configuration, and self-optimization. The complexity is characterized by the amount of processing and storage resources and implementation requirements. This work represents the first endeavor to analyze the level of automation and complexity of security solutions in SDN environments. Our findings reveal important advances in the area of security automation in SDN. However, there are still several open problems and challenges, which we detail in this work.

Cloud Computing provides an auto-scaling feature for dynamic resource utilization to cope with their customers’ requirements and charge as ‘pay-per-use’. Attackers get the benefit of this auto-scaling feature by flooding DDoS attacks on the VMs or instances in the cloud environments. Such DDoS attacks give financial damages to the customers because of massive amount of resource utilization, known as the Economic Denial of Sustainability (EDOS) attack. Transmission Control Protocol (TCP) SYN flooding attack is known to be the most challenging attack resulting EDOS attack. Software Defined Network (SDN) being a cost-efficient solution for cloud service providers, uses the OpenFlow switches and flow tables to make rules for each incoming flow. In SDN, it is feasible to classify an incoming flow as an attack and block forwarding of this flow to the targeted VM. This research work proposes an SDN based fast and computationally cost-efficient statistical anomaly detection model, named EDOS-TCP SYN mitigation model (EDOS-TSM) to mitigate TCP SYN flooding attack from a single user and spoofed IPs. EDOS-TSM uses binomial probability, TTL field value of IP packet header and multi-TCP SYN requests to detect source-based and spoofing based attacks. The proposed model is implemented, and the performance is evaluated on an OpenStack production-based cloud with real-time traffic and attack generation. The attack mitigation results are compared with existing models in the literature. The results show a large number of false negatives in existing models and efficiency of EDOS-TSM is proved in both source based and spoofing attacks.

Software Defined Networking (SDN) has emerged as a promising paradigm offering an unprecedented programmability, scalability and fine-grained control over forwarding elements (FE). Mainly, SDN decouples the forwarding plane from the control plane which is moved to a central controller that is in charge of taking routing decisions in the network. However, SDN is rife with vulnerabilities so that several network attacks, especially Distributed Denial of Service (DDoS), can be launched from compromised hosts connected to switches. DDoS attacks can easily overload the controller processing capacity and flood switch flow-tables. This paper deals with the security issue in SDN. It proposes a real-time protection against DDoS attacks that is based on a controller-side sliding window rate limiting approach which relies on a weighted abstraction of the underlying network. A weight defines the allowable amount of data that can be transmitted by a node and is dynamically updated according to its contribution to : (1) the queuing capacity of the controller, and (2) the number of flow-rules in the switch. Hence, a new deep learning algorithm, denoted the Parallel Online Deep Learning algorithm (PODL), is defined in order to update weights on the-fly according to both aforementioned constraints simultaneously. Furthermore, the behavior of each host and each switch is evaluated through a measure of trustworthiness which is used to penalize mis-behaving ones by prohibiting new flow requests or PacketIn messages for a period of time. Host trustworthiness is based on their weights while switch trustworthiness is achieved through a computation of the Average Nearest-Neighbor Degree (ANND). Realistic experiments show that the proposed solution succeeds in minimizing the impact of DDoS attacks on both the controllers and the switches regarding the PacketIn arrival rate at the controller, the rate of accepted requests and the flow-table usage.

Low-rate Denial of Service (LDoS) attacks cause severe destructiveness to network security. Consequently, the implementation of detection and defense against them is a concern among the research communities. But it is formidable to deploy extension modules to detect and mitigate attacks online in traditional networks, because devices are deficient of flexibility and scalability. To address the problem, we design and implement an online attack detection and mitigation system (ADMS) framework via the scalable and programmable Software Defined Networking (SDN). ADMS is installed on SDN controllers and conforms to the OpenFlow policy without extra devices. ADMS consists of two modules: the two-phase detection module and the mitigation module. The two-phase detection module combines the new port traffic feature and the Lightgbm classifier based on flow table statistics traffic to precisely detect LDoS attacks. The mitigation module utilizes the novel Sequence Matching based Dynamic Series Analysing (SMDSA) algorithm to locate the attacker, and efficiently mitigates attack traffic by packet filter. The SMDSA algorithm distinguishes the victim port from benign ports by calculating the anomaly score of each port. Our evaluation on a prototype implementation of ADMS shows that the framework is able to precisely identify and efficiently mitigate LDoS attacks in real-time.

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

Traffic management in software-defined networks (SDNs) is critical for efficient bandwidth utilization and resource provisioning. Recent works on SDN load balancing (LB) have focused on identifying and rerouting elephant flows (EFs) for effective bandwidth usage. These techniques have some limitations, such as using source-to-destination hop count as the primary rerouting metric and not differentiating the types of flow that result in frequent resource conflicts when handling EF with long-lived bandwidth. Besides, current EF detection techniques use predefined bandwidth-use thresholds that cannot adapt to the ever-changing traffic condition. Also, detecting EF on switches results in high controller-switch bandwidth and high EF detection time. This study presents an ant colony optimization-based technique for rerouting EFs while considering load-balancing in the SDN links. This technique, called DPLBAnt, is formulated as a shortest-path problem in SDN that can alleviate the high controller-switch load. The proposed technique first detects EF by using a pair of classifiers on both SDN controller and switches. Most EF candidates are sifted on the switches, resulting in accurate and efficient detection of EF. Then, DPLBAnt obtains the global state of the SDN from which the most optimal paths for congested links are retrieved, and EF are redirected accordingly. The performance of the proposed DPLBAnt has been extensively simulated. Results indicate its superior performance over Equal-Cost Multi-Path (ECMP) and FlowSeer techniques in terms of average end-to-end delay (54% and 7.9% better), average network throughput ( 3 . 5 × and 1 . 5 × better), and average packet loss (18% and 10% better) respectively. The overall performance indicates that the proposed LB technique based on detection and rerouting of EFs can improve SDN’s overall performance.

[EN] Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical control is centralized in a controller. However, the centralized control logic can be an ideal target for malicious attacks, mainly Distributed Denial of Service (DDoS) attacks. Recently, Deep Learning has become a powerful technique applied in cybersecurity, and many Network Intrusion Detection (NIDS) have been proposed in recent researches. Some studies have indicated that deep neural networks are sensitive in detecting adversarial attacks. Adversarial attacks are instances with certain perturbations that cause deep neural networks to misclassify. In this paper, we proposed a detection and defense system based on Adversarial training in SDN, which uses Generative Adversarial Network (GAN) framework for detecting DDoS attacks and applies adversarial training to make the system less sensitive to adversarial attacks. The proposed system includes well-defined modules that enable continuous traffic monitoring using IP flow analysis, enabling the anomaly detection system to act in near-real-time. We conducted the experiments on two distinct scenarios, with emulated data and the public dataset CICDDoS 2019. Experimental results demonstrated that the system efficiently detected up-to-date common types of DDoS attacks compared to other approaches., This work has been partially supported by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant of Project 310668/2019-0 and by SETI, Brazil/Fundacao Araucaria due to the concession of scholarships; by the "Ministerio de Economia y Competitividad, Spain"in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento"within the project under Grant TIN2017-84802-C2-1-P.

In the world of service-oriented businesses the deployment of services is of utmost importance. On the Web, the service-oriented business has a major impact through Web Application Program Interfaces (APIs). In the field of Web applications, majority of the applications run on the Web/Internet APIs. These APIs are dependent on architecture styles such as Representational State Transfer (REST) and Remote Procedure Call (RPC). Over a decade, REST is widely used for architectural style in API design followed by RPC. These styles are used extensively in various domains. This paper presents the survey of such APIs in the world of Software-Defined Networks (SDN) and Network Function Virtualization (NFV). The main contributions of the paper is to provide a ready reference for developing effective and efficient orchestration approaches towards micro services in SDN and NFV domains. The survey aims to help practitioners and researchers in this field to follow guidelines and standards for designing higher quality services.

In Software-Defined Networks (SDN), one of the critical research challenges for large-scale deployment of SDN is controller placement problem (CPP). However, link failures are critical security issues in networks and greatly impact SDN’s robustness. The CPP for link failures is still challenging today. To this end, we study the CPP for multi-link failures (CPP-MLF). Firstly, we investigate the real link data of today’s networks to analyze the characteristics of link failures and formalize the link failure rate. Next, we formulate minimizing the number of controllers and worst-case delay as the optimization objectives of CPP-MLF and develop a heuristic algorithm based on improved NSGA-II to solve CPP-MLF efficiently. Specifically, we improve the non-dominated set and crowding distance for the NSGA-II by dynamic competition mechanism and even distribution operator, respectively, thereby quickly obtaining the Pareto-optimal solutions of CPP-MLF. Afterwards, we introduce controller load variance to evaluate them to make a satisfactory decision on controller placement. We conduct simulations with real network topologies, and the results show that the proposed heuristic algorithm performs well on the number of controllers, worst-case delay and robustness, while producing acceptable runtime overheads.

Software Defined Networking (SDN) is a new networking paradigm where forwarding hardware is decoupled from control decisions. It promises to dramatically simplify network management and enable innovation and evolution. In SDN, network intelligence is logically centralized in software-based controllers (the control plane), while network devices (OpenFlow Switches) become simple packet-forwarding devices (the data plane) that can be programmed via an open interface (OpenFlow protocol). Such decoupling of the control plane from the data plane introduces various challenges that include security, reliability, load balancing, and traffic engineering. Dreadful security challenges in SDNs are denial of service (DoS) and distributed denial of service (DDoS) attacks. For instance, in SDNs, DoS/DDoS attacks could flood the control plane, the data plane, or the communication channel. Attacking the control plane could result in failure of the entire network, while attacking the data plane or the communication channel results in packet drop and network unavailability. In this paper we deliver several contributions that shed light on the field of DoS/DDoS attacks in SDNs, providing a complete background about the area, including attacks and analysis of the existing solutions. In particular, our contributions can be summarized as follow: we review and systematize the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches. Moreover, the discussed countermeasures are organized accordingly to their focus, be it on detection, mitigation, prevention, or graceful degradation. Further, we survey the different approaches and tools adopted to implement the revised solutions. Finally, we also highlight possible future research directions to address DoS/DDoS attacks in SDNs.

With the rapid development of the edge computing, Internet economy has become a new situation in economic development. What has brought development to the Internet economy is a variety of family-based platforms (i.e., edge computing). However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform allocations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of SDN (software defined network) platforms, it is a huge challenge to reasonably allocate e-commerce resources/platforms to users. The quality of the SDN personalized resource allocations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can allocate the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized allocation faces the problem of too high user data dimensions. Therefore, we first conduct computational efficiency analysis toward each user. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that, in the edge computing context, our method can reduce the complexity of resource allocation toward complex data and improve the quality of the allocated data.

Reliability, and safety are considered as the indispensable twins during the adoption of the cloud computing environment since their breaches lead to catastrophic issues in poor resource management and unreliable service quality. To be specific, Distributed Denial of Service (DDoS) attack is determined as the most vulnerable threat in the cloud space as it lowers the ability of the predominant resources’ for preventing their optimal utilization. The advent of Software-Defined Networking (SDN) is estimated to wide open the feasibility in preventing DDoS attacks in the cloud space. In this paper, a Rival-Model Penalized Self-Organizing Map (RMP-SOM) enforced DDoS Attack Prevention Mechanism is proposed for the remarkable prevention of DDoS attack by utilizing the potential characteristics of SDN that focuses on the possibility of facilitating network global perspective, effective investigation of network traffic, and an enhanced process of rule updating. This proposed RMPSOM-SDNDM scheme utilizes the benefits of the constant rate in order to ensure priority to the closest neuron and its neighborhood rather than its farthest rival neuron for facilitating better detection accuracy. The simulation results of the proposed RMPSOM approach confirmed a phenomenal sensitivity, specificity and accuracy rate during the process of detecting DDoS attacks in the cloud space on par with the baseline DDoS mitigation schemes considered from the literature.

With the rapid development of Internet-of-Things (IoT), more smart devices can be connected to the Internet, resulting in a dramatic increase of data transmission and communication. Software-Defined Networking (SDN), which separates the control planes and data planes, is considered as a promising solution to provide the scale and versatility necessary for IoT. However, SDN still suffers from several challenges, i.e., the centralized control plane would be a single point of failure. With the wide adoption of blockchain applications, such technologies can have a positive impact on SDN’s performance, i.e., blockchains allow non-confident individuals to interact with each other without the need for a central authority. However, attackers can still inject traffic to influence blockchain nodes from normal operations. Motivated by the recent development of blockchains and SDN, in this work, we focus on blockchain-based SDN and develop BSDNFilter, an IDS-based security mechanism that builds a trust-based filtration by using traffic fusion and aggregation to handle and reduce malicious traffic. Through collaborating with an IT organization, our evaluation in a real blockchain-based SDN environment demonstrates that our BSDNFilter is able to achieve better filtration performance against flooding attacks than similar approaches.

Fog computing is a promising paradigm aimed at reducing latency and network traffic between end devices and cloud servers. In fog computing systems, a fog node can be overloaded by an increased number of requests from end devices. In this situation, an efficient offloading mechanism among fog nodes is necessary to meet the requirements of fog computing. Software Defined Networking (SDN) has become the de facto standard for future network infrastructures, including fog computing systems. In this paper, we propose a dynamic offloading service among fog nodes for an SDN-based fog computing system that aims at selecting an optimal offloading node and assisting the offloading path by providing an end-to-end bandwidth guarantee based on SDN technology. The proposed solution outperforms traditional approaches without SDN technology in terms of both throughput and request response time by selecting the appropriate offloading node using current computational and network resource information and providing a reliable end-to-end path.

This paper proposes a routing scheme for full-duplex-(FD)-based multi-hop CRNs that attempts to maximize the packet-delivery-ratio between any source–destination pair by being aware of the time-varying nature of spectrum-availability of the operating environment. The proposed routing scheme consists of path selection and channel assignment. Two design variants are presented: one for inband- and the other for outband-FD CRNs. Our scheme is performed into three phases: path-discovery (to identify a set of feasible routes), channel-selection (to assign channels along each path) and path-selection (to select the highest effective availability-time path). Compared to a reference scheme, the simulation results indicate that being spectrum-availability-aware can achieve significant performance enhancement.

Software Defined Networking (SDN) is a promising technology that provides flexibility, programmability, and network automation by shifting the network intelligence to the logically centralized controller. In SDN architecture, the controller maintains the global view of network topology; therefore the network management is efficient as compared to the traditional networks. Moreover, the cost of SDN devices is less due to the use of open source network operating system instead of proprietary and vendor-specific software. In spite of the flexibility offered by the SDN, OpenFlow enabled switches have a fixed behavior as specified in the datasheet of switch ASIC. They recognize fixed set of header fields according to the support of OpenFlow version. In addition, SDN is suffering from scalability and performance issues because SDN switches heavily dependent on the control plane to forward the packets which increases the data-control communication overhead. To resolve these issues, P4-Programmable data plane switches can be used. The analysis of review articles about SDN suggests insufficient focus on the data plane programmability. Therefore, this paper provides the comprehensive overview of domain-specific language (P4) for the programmability of the data plane. We have discussed the problems in the traditional SDN architecture and then, how these problems can be managed by the data plane programmability. Further, this study critically analyze the research articles based on the P4 programming language for network traffic monitoring, DDoS attack detection, load balancing, and packet aggregation and disaggregation. Finally, we have identified the research gaps and highlighted the open research challenges in the field of data plane programmability for the future directions.

The modern paradigm of Software Defined Network (SDN) inspired the research community to develop more innovative and transformative solutions for large scale and complex crucial Cyber Physical Systems (CPS). However, the recent destructive attacks targeting such CPS applications, raised the need to initially secure and scale the core SDN network. These attacks may target the heart of SDN networks itself formed by the controllers. Therefore, recent research works discussed the Controller Placement Problem (CPP) to determine the required number of SDN controller(s) and their optimal locations in synonyms wide area networks. A few of the presented solutions discussed the network security aspect to solve this problem. In addition, none of them addressed the need to secure and dynamically change SDN controller location to confuse attackers. Therefore, in this paper, we introduce a new concept of Controller Placement Camouflage (CPC) to dynamically change the attack surface (SDN controller placements) for Moving-target Defense (MTD). The new paradigm of MTD imposes various asymmetric offensive and defensive modes against cyber-adversaries. We relied on the Zero-Sum game as a stochastic game between the system defender and the attacker to guide our MTD solution. In addition, our presented solution is adapted to consider the network vulnerabilities, evaluate the risk level of the system in real-time using Bayesian Attack Graph (BAG), to frequently shift the SDN controller(s) location. Furthermore, we used the Smart Grid as a case study for a mission critical CPS application. We conducted a scenario of UK Bulk Demand Points (BDP), 50MVA generation network capacity map to simulate a real mini-grid network topology. To the best of our knowledge, this research work may be considered the first introduction for SD-CPC for MTD.

In Software-Defined Networking (SDN) the controller is the only entity that has the complete view on the network, and it acts as the brain, which is responsible for traffic management based on its global knowledge of the network. Therefore, an attacker attempts to direct malicious traffic towards the controller, which could lead to paralyze the entire network. In this work, Deep Learning algorithms are used to protect the controller by applying high-security measures, which is essential for the continuous availability and connectivity in the network. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are proposed to recognize and prevent the intrusion attacks. We evaluate our models using a recently released dataset (InSDN dataset). Finally, our experiments manifest that our models achieve very high accuracy for the detection of Denial of Service (DoS) attacks. Thus, a significant improvement in attack detection can be shown compared to one of the benchmarking state of the art approaches.

International audience; Software Defined Networking is a concept within the networking field which proposed a centralized control considering the control and data planes. To overcome the safety and security threats, solutions might be divided into two categories: enforcing the controller to make it more robust or the architecture using a multi-controller approach. This work aims to pave the way for a multi-controller architecture without East-West interface to avoid the spreading of an attack. There is one nominal controller in charge of the control while the second observes the traffic at the Southbound interface to detect anomalies of control. A detection method is introduced theoretically and relies on Intrusion Detection System theory, more precisely the specificationbased. Here, the specification is a template determined through a projection function of the control logic. The template is compared to the activity of the command observed such that any deviation generates an alarm. The method is finally explained in use cases.

Software Defined Networks (SDN) is a networking paradigm that separates the control plane from the forwarding plane. There is little research on structuring the SDN data plane for security. The Robust Network and Segmentation Algorithm (RNS) is an algorithm based on Product Family Algebra (PFA) that implements layered defence and segmentation strategies to segment resources towards designing secure networks. In this paper, we present an additional plane in charge of the configuration and governance of SDN data planes that we call Dynamic Configuration and Governance (DCG) plane. It is intended to give agility to dynamic networks. It implements the RNS algorithm in SDN environment. Moreover, we propose and suggest three architectures that use DCG plane. Then we assess the three architectures. The assessment results identify an architecture that is suitable for dynamic networks and another for networks that are more stable regarding changes to policy and network topology.

The emergence of Software Defined Networking (SDN) increases the flexibility of routing and provides an efficient approach to balance network flows. Due to the economical and technical challenges in transiting to a full SDN-enabled network, a hybrid SDN, with a partial deployment of SDN switches in a traditional network, has been a prevailing network architecture. For a hybrid SDN, the routing flexibility is influenced by the expensive and limited Ternary Content Addressable Memory (TCAM) resource, where SDN switches store the flow entries dispatched from SDN controllers. Considering the limited TCAM resource, to reduce the abundant flow entries in TCAM, we propose to impose the constraints on the number of routing paths (i.e., path cardinality constraints) when optimize flows routing. To solve this problem, in this paper, we first formulate the routing optimization problem with the path cardinality constraints in a hybrid SDN as a Mixed Integer Non-Linear Programming (MINLP) problem. Then, we propose an incremental deployment method for obtaining a hybrid SDN and an H-permissible Paths Routing Scheme (HPRS) to effectively route traffic flows under the path cardinality constraints. After that, the theoretic analysis is given to prove that the approximation ratio of HPRS is O ( l o g L ) . Finally, through extensive experiments, we demonstrate that our proposed algorithm HPRS can efficiently reduce flow entries and approximates optimal routing under different network topologies.

The power consumed by the Internet industry has been growing sharply in recent years, which accounts for a considerable proportion of the overall power consumption. To reduce high power consumption in the network, numerous proposals on power-saving are proposed. However, they usually reduce power consumption by dynamically sleeping and awakening the network device according to the current traffic fluctuation. When the traffic fluctuate sharply, the network device will swing between sleep and active state frequently, which consumes a considerable power and causes a series of side effects, such as packet losses, short sustainable device service life and synchronization among devices. In this paper, based on Software Defined Networking (SDN), we propose a Power Optimization with Less State Transition (POLST) framework, which mainly achieves the following two functions: (1) Real-time network monitoring with high accuracy and low overhead; (2) Power optimization with less device state transition. Then, we formulate optimization problem of minimizing the network power consumption, while considering power consumed by device state transition. To solve it, we propose a Power Aware Routing with Less State Transition (PARLST) algorithm. Finally, simulation results show that compared with the benchmarks, POLST has excellent performance in both network monitoring and network power optimization. More specifically, in the network monitoring, POLST can achieve up to 97.2% measurement accuracy with low monitoring overhead. In the power optimization, in comparison to the benchmarks, POLST can achieve up to 72.2% energy efficiency, 56.6% decrease in Switch State Transition (SST), 53.5% decrease in Link State Transition (LST).

Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration while minimizing its cost across the edge nodes, in this paper, we leverage reinforcement learning (RL) method to design a cost-effective framework, called Mig-RL, for the service migration with a reduction of total service costs as a goal in a mobile edge environment. The Mig-RL leverages the infrastructure of edge network and deploys a migration agent through Q-learning to learn the optimal policy with respect to the service migration status. We distinguish the Mig-RL from other existing works in several major aspects. First, we fully exploit the nature of this problem in a modest migration space, which allows us to constrain the number of service replicas whereby a defined state–action space could be effectively handled, as opposed to those methods that need to always approximate a huge state–action space for policy optimality. Second, we advocate a migration policy-base as a cache to save the learning process by retrieving the most effective policy whenever a similar migration pattern is encountered as time goes on. Finally, by exploiting the idea of software defined network, we also investigate the efficient implementation of Mig-RL in mobile edge network. Experimental results based on some real and synthesized access sequences show that Mig-RL, compared with the selected existing algorithms, can substantially minimize the service costs, and in the meantime, efficiently improve the QoS by adapting to the changes of mobile access patterns.

In the context of wide area networks (WANs) and software defined networking (SDN), reducing the communication delays experienced by the network devices is an important challenge whose solution requires a careful placement of controllers to decrease the end-to-end latencies. Although the majority of studies focusing on the controller placement problem (CPP) only consider the switch–controller propagation delays and inter-controller latencies, the controllers’ capacity needs to be addressed for lowering the end-to-end delays. While the controllers with a variety of processing rates, number of ports, and costs are available in the market, Internet Service Providers (ISPs) need to consider the affordability and capability of compensating their needs by maximizing the use of their networks’ resources. To propose a solution for this important problem, we consider the Knapsack 0–1problem and formulate the Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP), a meta-heuristic algorithm, with new iterations and temperate mating conditions to solve the CPP. This algorithm uses a reasonable amount of computation time to obtain the minimum delays. A number of Topology-Zoo datasets are analyzed with a variety of small to large scale data plane nodes to evaluate the GSOCCPP algorithm based on different controllers’ capacities. The simulation results demonstrate that, in addition to outperforming similar meta-heuristic and clustering algorithms such as the Firefly Algorithm, Particle Swarm Optimization, and the k-means++, our newly proposed GSOCCPP algorithm is successful in achieving the lowest execution time among the analyzed algorithms. Furthermore, this proposed solution has a more efficient memory consumption compared to other algorithms for controller placement in different network topologies.

Link fault tolerance has been a hotspot in Software-Defined Networking (SDN) for many years. Both proactive and reactive schemes are proposed to guarantee network availability and robustness. However, the former wastes plenty of Ternary Content Addressable Memory (TCAM) and bandwidth resources, while the latter faces long fault recovery time. In this paper, we propose FTLink, an efficient and flexible link fault tolerance scheme in SDN. Firstly, through collecting the current network state information, we pre-generate a set of backup links for each link of the primary path of the flow. We formulate the backup links planning as a multi-objective optimization problem that minimizes the required switch TCAMs and link bandwidths. Combining flow characteristics, we develop a two-step heuristic algorithm, namely planning backup links for each link that transmits elephant flows with the greedy tracing method in step 1 and for each link that transmits mice flows with the bidirectional searching method in step 2, to determine the backup links set. Then, we introduce a global matching table in the controller to maintain the generated backup links and additional flow rules. Once monitoring a faulty link, FTLink looks up the matching table entries and enables the backup links via installing the additional flow rules into the switches to realize flexible fault recovery. Simulations show that FTLink is effective in terms of high-efficiency TCAM and bandwidth usage and produces an acceptable recovery time of less than 30 ms, compared with baseline schemes. The prototype tests under realistic network further confirm the efficiency of FTLink.

To ensure the quality of service of an end-to-end connection, current network solutions are mostly dependable on the differentiation between different classes of traffic. The Software-defined networking (SDN) architecture has emerged to offer network programmability, giving to network operators a programmatic control over their network. In SDN, network devices are programmed in many ways, having a standard, open, and vendor-agnostic interface, e.g., OpenFlow, enabling the control plane to instruct the forwarding behavior of network devices from different vendors. In this paper, we introduce the Programmable Labels (ProgLab) approach to support traffic differentiation with QoS guarantees as a low-cost alternative built over an SDN architecture. The idea relies on the simplification of a packet-forwarding operation which relies on the remainder of a division, instead of classical table lookup method. ProgLab computes programmable label at the control plane by solving a congruence system from Residue Number System and the co-prime numbers assigned to the switches in the path of an end-to-end connection. Such label has a meaning within this network that expresses the entire route, addressing the respective traffic class at each switch’s logical queue along the path. ProgLab approach has been implemented through the P4 language and evaluated through an emulation-based evaluation. The experiments demonstrated the feasibility of ProgLab and showed its ability in providing QoS differentiation on demand.

Achieving high programmability has become an essential aim of network research due to the ever-increasing internet traffic. Software-Defined Network (SDN) is an emerging architecture aimed to address this need. However, maintaining accurate knowledge of the network after a failure is one of the largest challenges in the SDN. Motivated by this reality, this paper focuses on the use of self-healing properties to boost the SDN robustness. This approach, unlike traditional schemes, is not based on proactively configuring multiple (and memory-intensive) backup paths in each switch or performing a reactive and time-consuming routing computation at the controller level. Instead, the control paths are quickly recovered by local switch actions and subsequently optimized by global controller knowledge. Obtained results show that the proposed approach recovers the control topology effectively in terms of time and message load over a wide range of generated networks. Consequently, scalability issues of traditional fault recovery strategies are avoided.

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage- intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI. In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

Software-defined networking (SDN) is an emerging networking technology, which has attracted wide attention from academia and industry, playing a key role in enabling techniques of the 5th generation wireless systems (5G). The fundamental characteristic of SDN is that it decouples the control plane from the data plane, which can provide flexibility and programmability for 5G. Unfortunately, the separation of the two planes becomes a potential attack surface as well, which enables adversaries to fingerprint and attack the SDNs. Existing work showed the possibility of fingerprinting an SDN with time-based features. However, they are coarse-grained. This paper proposes a fine-grained fingerprinting approach that reveals the much more severe threats to SDN security and explores the mitigation strategies. By analyzing network packets, the approach can dig out sensitive and control-related information, i.e., match fields of SDN flow rules. The match fields of flow rules can be used to infer the type of an SDN controller and the security policy of an SDN network. With sensitive configuration information, adversaries can launch more targeted and destructive attacks against an SDN. We implement our approach in both simulated and physical environments with different kinds of SDN controllers to verify the effectiveness of our concept. Experimental results demonstrate the feasibility to obtain fine-grained and highly sensitive information in SDN, and hence reveal the high risk of information disclosure in SDN and severe threats of attacks against SDN. To mitigate the fine-grained fingerprinting threat we have revealed, we explore a lightweight countermeasure trying to hide the sensitive time-based features of SDN networks. Implementation and evaluation demonstrate that our countermeasure can play a role in mitigating the risk of SDN control information leakage with only minor overheads.

Currently, the increasing demands of user terminals have surged drastically and pulled up the global data traffic along. According to 3rd Generation Partnership Project (3GPP), offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE–WLAN Aggregation (LWA) was introduced by 3GPP that is apparently suitable for the Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, a mathematical model is formulated that uses Karush–Kuhn–Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput.

With the evolution of the concept of Internet into the Internet-of-Things (IoT), in which sensors, devices, and everyday objects (things), are interconnected through the Internet, a large number of interconnected things are expected to produce massive amounts of data, which needs efficient processing in order to provide various services. IoT is of little worth without the ability to process, manage, and control a large amount of heterogeneous data in a timely and efficient manner. The concept of Data Fusion (DF) is based on combining heterogeneous multi-source data in order to produce high quality information, and reduce network traffic. On the other hand, the highly dynamic and heterogeneous nature of IoT requires very flexible and efficient management. The recent software defined systems paradigm is an appealing solution to reduce complexity and overhead in managing complex systems such as IoT by separating the control plane from the data plane. In this work, we present a novel experimental framework for IoT based environmental monitoring applications using concepts from DF and software defined systems (SDS). It is built on top of the software defined networking platform where the core components (the host, switch and the controller) are expanded to support other software defined systems components (such as software defined storage and security) and enable the applications of different DF techniques in IoT environments. We conduct several experiments to highlight features of the presented framework.

Wireless body area networks (WBANs) are usually used to collect and monitor health-related information for both critical and non-critical patients. However, the traditional WBAN communication framework is unable to guarantee the successful delivery of critical information due to a lack of administrative control and priority support for emergency data. To overcome these issues, this paper proposes a novel software-defined networking (SDN)-based WBAN (SDWBAN) framework for application-specific traffic management. An application classification algorithm and a packet flow mechanism are developed by incorporating SDN principles with WBAN to effectively manage complex and critical traffic in the network. Furthermore, a Sector-Based Distance (SBD) protocol is designed and utilized to facilitate the SDWBAN communication framework. Finally, the proposed SDWBAN framework is evaluated through the CASTALIA simulator in terms of Packet Delivery Ratio (PDR) and latency. The experimental outcomes show that the proposed system achieves high throughput and low latency for emergency traffic in SDWBANs.

The distributed control plane of Software-Defined Networks improves the scalability of the control plane and enables to dynamically assign switches to controllers. However, existing works calculate the assignment mainly based on the number of flow path setup requests generated from the switches and neglect the different resource demand of the requests on the controllers. To address this problem, we analyze the controller resource demands of the requests of different paths and formulate a flow path-aware switch assignment problem to minimize the controller resource demand. To efficiently solve the problem, we propose a heuristic algorithm which is inspired by the rearrangement inequality theory to accommodate to the frequent flow fluctuation. Simulation results show that our solution can save the controller resources significantly compared with the existing solutions.

Cloud has become a dominant computing platform, and cloud data centers have been widely deployed all over the world. Naturally, cloud data centers become the targets of cyber attacks due to the feature of publicity. In addition, the price of renting resources from cloud constantly gets cheaper and cheaper. Therefore, attackers can rent hosts from cloud data centers to initiate attacks with rather low cost. As a result, hosts in a cloud center could be either victims or attackers. However, most existing researches only treat the hosts as the targets or the sources of attacks, either protecting the hosts from being attacked or identifying the malicious hosts, which is insufficient to protect the cloud data centers comprehensively. In this paper, we hire the novel techniques of SDN to protect the cloud data centers in both directions. Aiming at mitigating DDoS attacks, we propose HostWatcher, a system that watches and protects every host in cloud data center. HostWatcher leverages the advantages of SDN techniques and distributed processing. Caching and round-robin-resending scheme is introduced to the proposed system. Our goal is to protect the hosts comprehensively with QoS guarantee. The extensive experiments show that HostWatcher can effectively mitigate the DDoS attacks that target the hosts. Meanwhile, HostWatcher can also significantly limit the packet rate of hosts that are controlled by attackers. Also, the comprehensive evaluations show that the overheads of our system are trivial, and that our system is practical to implement and deploy in the cloud data centers.

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.

Wireless networks show several challenges not found in wired networks, due to the dynamics of data transmission. Besides, home wireless networks are managed by non-technical people, and providers do not implement full management services because of the difficulties of manually managing thousands of devices. Thus, automatic management mechanisms are desirable. However, such control mechanisms are hard to achieve in practice because we do not always have a model of the process to be controlled, or the behavior of the environment is dynamic. Thus, the control must adapt to changing conditions, and it is necessary to identify the quality of the control executed from the perspective of the user of the network service. This article proposes a control loop for transmission power and channel selection, based on Software Defined Networking and Reinforcement Learning (RL), and capable of improving Web Quality of Experience metrics, thus benefiting the user. We evaluate a prototype in which some Access Points are controlled by a single controller or by independent controllers. The control loop uses the predicted Mean Opinion Score (MOS) as a reward, thus the system needs to classify the web traffic. We proposed a semi-supervised learning method to classify the web sites into three classes (light, average and heavy) that groups pages by their complexity, i.e. number and size of page elements. These classes define the MOS predictor used by the control loop. The proposed web site classifier achieves an average score of 87 % ± 1 % , classifying 500 unlabeled examples with only fifteen known examples, with a sub-second runtime. Further, the RL control loop achieves higher Mean Opinion Score (up to 167% in our best result) than the baselines. The page load time of clients browsing heavy web sites is improved by up to 6.6x.

This paper discusses a novel frame work approach of Software Defined Network based prevention on phishing attack with the help of the deep machine learning with CANTINA approach (DMLCA) in the cyberspace. Cyber security is a significant concern in the operations of coalition, and is a complex challenge because of some needs in operational effectiveness and also the trust relationship limit which exists over the coalition partners. In networking, new promising paradigms like Software Defined Networks (SDN), offer a method to deal more efficiently with their security constraints. This machine learning approach is to deal with the phishing attack problem based on the SVM (support vector machine) and this machine learning technique with SVM helps to effectively to solve classification problems. The CANTINA approach helps to support the robust hyperlinks with the help of evaluating the term frequency (TF) and inverse document frequency and (IDF). This information retrieval algorithm helps to compare, classify and retrieve various documents. The objective is to improve the detection accuracy with the help of the DMLCA method with the various parameters such as detection accuracy based on the true positive ratio and false positive ratio, precision and recall.

There has been an enormous growth in networking and data communication between the wireless devices. Cloud computation has taken a giant leap into the mobile domain to cater to the on-going communication demands. The latest programming techniques have also paved the way for incorporating Software Defined Networking techniques in Mobile Cloud environment. The growth of networking infrastructure with many such technological innovations, has led to the parallel increase in security and privacy of data at different levels of communication. The communication links and interfaces have become targeted platform for various security attacks causing denial of services. This survey comprehends an in-depth analysis of the possible data privacy attacks and threats in the proposed Software Defined Mobile Cloud. The possibilities of the attacks which are enlisted focus on various ways of spoofing and flooding of data packets, mis-interpretation and aggregation of the flow rules for communication, packet dropping and traffic flow enrouting at different cross-layered architectural levels built over the heterogeneous networks. An overview of the mechanisms to encounter the various vulnerabilities in the suggested framework is recommended. This article further identifies research directions and challenges to incorporate the possible techniques and preventive solutions, to overcome the malicious attacks in the software defined mobile cloud.

Software Defined Networking achieves programmability of the control plane by separating it from the data plane. In this paper, we propose a strategy for controller placement to minimize the maximum load imbalance between controllers while enforcing the placement and balancing constraints. We also propose a scalable algorithm to compute near optimal solutions of the problem on large scale networks. The algorithm uses poly-stable matching to distribute a fraction of switches equally among controllers so as to reduce the load imbalance. The remaining switches are assigned to their nearest controllers while considering latency of switches and load of controllers. Further, the algorithm relocates some of the switches from the controller to which they are currently assigned so as to reduce the latency between switches and controllers. The proposed algorithm is evaluated on widely used Chinanet and Interoute networks extracted from internet topology zoo. The results show that the proposed algorithm outperforms the existing controller placement solutions for software defined wide area networks in terms of load imbalance and inter controller latency without affecting the average and standard deviation in switch to controller latency.

One of the main challenges for developed cities is vehicle traffic and its management. Because of physical structures and cross roads of urban intersections, traffic flows may cause time delay, congestion, traffic accidents and more fuel/time consumption. For these reasons, there are many studies on traffic management systems which are a part of smart city applications in both literature and practice. In recent years, it seems that VANET (Vehicular Ad Hoc Networks) architecture has been preferred in these studies, which enables vehicles to easily communicate with each other or devices on the edge of the road and also to transfer the related traffic data to the center. On the other hand, there are many studies on a new network paradigm called SDN (Software Defined Networks) that solves problems such as performance, programmability, scalability, security and management difficulties in today's traditional networks. Depending on all this, in this study, we will present an architectural proposal about how to use SDN and VANET network paradigms together in traffic management systems in order to use both existing intersection management system modules more adaptively and future traffic based services to be added in desired service quality without changing the communication infrastructure. Also in this study, a 3-stage fuzzy-decision tree model was proposed for traffic signaling which is the most important problems of urban traffic by focusing environmental factors (accident situation, priority vehicle transition etc.) and activity information (road construction, meeting, train and bus schedule etc.) not only vehicle density unlike other signaling studies in literature. The proposed model also generates/sends a dynamic flow input to SDN agent vehicles and SDN agent RSU (Road Side Unit) in intersection for making VANET routing protocols (AODV-ad hoc on-demand distance vector, DSDV-destination-sequenced distance vector) selection automatically according to traffic incident and density. In order to measure the functionality of the SDN based VANET architecture and 3 stage fuzzy-decision tree model on it for the transmission infrastructure of the traffic management, performance tests were performed on different traffic and network scenarios on Adapazari City Centre Model. SDN based VANET architecture was compared with traditional systems according to transmission infrastructure and also proposed 3-stage fuzzy-decision model was compared with signaling techiques in literature such as fixed-time signaling, Webster equation, ant colony algorithm and particle swarm optimization under same traffic and network scenarios. The results obtained from performance tests shows that the proposed 3-stage fuzzy-decision model has %15-17 better performance than fixed-time signaling and webster equation and also %9-11 better performance that particle swarm and ant colony optimizations in low density traffic scenarios. In high density and dynamic traffic scenarios these rates reach %20-22 compared to fixed-time signaling and webster equation and %14-15 compared to particle swarm and ant colony optimizations. In addition in this study, proposed SDN based VANET architecture was compared to traditional VANET ITS architectures within the same traffic and network scenarios. According to obtained results, it is shown that the proposed SDN based VANET has %30-40 better performance than traditional systems in end-end delay, throughput and packet loss rate criteria. (C) 2019 Elsevier B.V. All rights reserved.

SDN (Software Defined Network) has emerged as a revolutionary technology in network, a substantial amount of researches have been dedicated to security of SDNs to support their various applications. The paper firstly analyzes State-of-the-Art of SDN security from data perspectives. Then some typical network attack detection (NAD) methods are surveyed, including machine learning based methods and statistical methods. After that, a novel tensor based network attack detection method named tensor principal component analysis (TPCA) is proposed to detect attacks. After surveying the last data-driven SDN frameworks, a tensor based big data-driven SDN attack detection framework is proposed for SDN security. In the end, a case study is illustrated to verify the effectiveness of the proposed framework.

In Recent days, social networks, web applications and mobile platforms make consumers highly vulnerable. A modern approach requires more time to detect the attacks. However, the system has a very restricted knowledge of network malware behavior. It is essential for defenders to know malware behaviors, such as patterns of propagation or membership recruitment, the size of botnets, and distribution of bots, in order to fight against cyber-crime. Where, there is no strong knowledge of the size and allocation of malware in existing systems. To overcome the issues, the proposed method is designed efficient and secure trusted routing mechanism (ESTRM), which promotes a node to choose a different path if the present path often fails to deliver information to the destination. The proposed technique provides privacy from serious assaults for software defined networks by replaying routing data. Based on experiment evaluations, the proposed method improves 5.58 detection accuracy and reduces 0.35 true positive rate (TPR), 0.34 false positive rates (FPR) and 0.19 s execution time (ET) compare than conventional methods.

In recent years, with the rapid development of information transfer technology, the load balancing scheduling of server cluster system must be optimized. In order to optimize the load of server, a load balancing scheduling strategy for server cluster system was proposed in this paper. A new scheduling weighted method was proposed, and the optimization of load allocation was realized. In order to verify the feasibility of the scheduling strategy, the results were tested from several aspects. Through a comprehensive test, the research of load balancing scheduling strategy optimization for server cluster system is feasible, which can further improve the server’s load capacity.

Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains, like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance, due to its wider applicability. Though the performance of WN is satisfactory, it still suffers from several challenges such as energy efficiency, reliability and security. This work attempts to render reliability to the wireless network as sensor physical node in network by detecting the faulty sensor nodes. The faulty sensor nodes degrade the performance of the complete sensor network and hence, it is necessary to detect the faulty sensor nodes to attain better Quality of Service (QoS) with software defined in network. The proposed method detects the faulty sensor nodes using software defined network approach by means of Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster–Shafer (DS) theory protocol approach. The performance of the proposed approach is observed to be satisfactory in terms of Quality of Service parameters of faulty node detection accuracy, energy consumption and network lifetime.

The Wireless Body Area Network (WBAN) gathers medical data from remote patients using wireless sensor nodes and transmits to the physician or server. The allocation of bandwidth to all network nodes demands the priority for communication because the high priority information presupposes people’s lives. The reduced propagation delay or nearly real-time transmission environment is necessary for top concern or chronic illness systems to transmit data from patients to the care worker comparing healthy patients. In indoor Wireless communication, particularly in hospitals, there is a risk of a significant packet error or failure during transmission. Based on the requirements of the studies, the dynamic cycle-based signal acquisition and bandwidth allocation technique created to reduce the emergency node propagation delay and enhance the signal quality of an emergency node. First, a patient’s priority-based data acquisition and sampling approach completed. Second, the calculation of duty cycle-based bandwidth requirements for non-emergency nodes made. Finally, a dynamic scheduling approach to the allocation of bandwidth using a regression method introduced. In this study, to regulate or handle a significant quantity of wireless node (1000 nodes, the abstract Software Defined Network (SDN) in the WBAN setting used. The controller monitors the network priority based on assigning the bandwidth to the particular nodes using the regression analysis technique.