Abstract: Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.

Abstract: The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.

Abstract: Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

Abstract: The trend towards the cloudification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. Follow-Me Cloud applies a Markov-decision-process-based algorithm for cost-effective performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either software defined networking technologies or the locator/identifier separation protocol. Numerical results from our analytic model for follow-me cloud, as well as testbed experiments with the two alternative follow-me cloud implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.

Abstract: In recent time, software defined networking (SDN) has evolved into a new and promising networking paradigm. In the SDN-based cloud, the essential features of SDN, including global view of the whole network, software-based traffic analysis, centralized control over the network, etc. can greatly improve the DDoS attack detection and mitigation capabilities of the cloud. However, integration of SDN in the cloud itself introduces new DDoS attack vulnerabilities. Limited flow-table size is a vulnerability that can be exploited by the adversaries to perform DDoS attacks on the SDN-based cloud. In this paper, we first discuss various essential features of SDN that makes it a suitable networking technology for cloud computing. In addition, we represent the flow table-space of a switch by using a queuing theory based mathematical model. Further, we propose a novel flow-table sharing approach to protect the SDN-based cloud from flow table overloading DDoS attacks. This approach utilizes idle flow-table of other OpenFlow switches in the network to protect the switch’s flow-table from overloading. Our approach increases the resistance of the cloud system against DDoS attacks with minimal involvement of the SDN controller. Thus, it has very low communication overhead. Our claims are well supported by the extensive simulation-based experiments.

Abstract: Recently, software defined networks (SDNs) and cloud computing have been widely adopted by researchers and industry. However, widespread acceptance of these novel networking paradigms has been hampered by the security threats. Advances in the processing technologies have helped attackers in increasing the attacks too, for instance, the development of Denial of Service (DoS) attacks to distributed DoS (DDoS) attacks which are seldom identified by conventional firewalls. In this paper, we present the state of art of the DDoS attacks in SDN and cloud computing scenarios. Especially, we focus on the analysis of SDN and cloud computing architecture. Besides, we also overview the research works and open problems in identifying and tackling the DDoS attacks.

Abstract: Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS)—in terms of faster responsiveness and data-specific complex analytics—has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud-based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation-based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation ${e}$ -healthcare systems.

Abstract: The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid also known as, smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication (SGC) system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SGC system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. By separating the control plane from the data plane, SDN helps the network operators to manage the network flexibly. Since SG heavily relies on communication networks, therefore, SDN has also paved its way into the SG. By applying SDN in SG systems, efficiency and resiliency can potentially be improved. SDN, with its programmability, protocol independence, and granularity features, can help the SG to integrate different SG standards and protocols, to cope with diverse communication systems, and to help SG to perform traffic flow orchestration and to meet specific SG quality of service requirements. This paper serves as a comprehensive survey on SDN-based SGC. In this paper, we first discuss taxonomy of advantages of SDN-based SGC. We then discuss SDN-based SGC architectures, along with case studies. This paper provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.

Abstract: Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.

Abstract: Network function virtualization (NFV) has been introduced by network service providers to overcome various challenges that hinder them from satisfying the growing demand for networking services with higher return-on-investment. The association of NFV with the leading technologies of information technology virtualization and software defined networking is paving the way for flexible and dynamic orchestration of the VNFs, but still, various challenges need to be addressed. The VNFs instantiation and placement problems on data center’s (DC) servers are key enablers to achieve the desired flexible and dynamic NFV applications. In this paper, we have addressed the VNF placement problem by providing a novel mixed integer linear programming (MILP) optimization model and a novel heuristic solution, Betweenness centrality Algorithm for Component Orchestration of NFV platform (BACON), for small- and large-scale DC networks. The proposed solution addresses the VNF placement while taking into consideration the carrier-grade nature of the NFV applications and at the same time, minimizing the intra- and end-to-end delays of the service function chain (SFC). Also, the proposed approach enhances the reliability and the quality of service (QoS) of the SFC by maximizing the count of the functional group members. To evaluate the performance of the proposed solution, this paper conducts a comparative analysis with an NFV-agnostic algorithm and a greedy-k-NFV approach, which is proposed in the literature work. Also, this paper defines the complexity and the order of magnitude of the MILP model and BACON. BACON outperforms the greedy algorithms especially the greedy-k-NFV solution and has a lower complexity, which is calculated as $O((n^{3}-n^{2})/2)$ . The simulation results show that finding an optimized VNF placement can achieve minimal SFCs delays and enhance the QoS accordingly.

Abstract: 5G mobile networks provide additional benefits in terms of lower latency, higher data rates, and more coverage, in comparison to 4G networks, and they are also coming close to standardization. For example, 5G has a new level of data transfer and processing speed that assures users are not disconnected when they move from one cell to another; thus, supporting faster connection. However, it comes with its own technical challenges relating to resource management, authentication handover and user privacy protection. In 5G, the frequent displacement of the users among the cells as a result of repeated authentication handovers often lead to a delay, contradicting the 5G objectives. In this paper, we propose a new authentication approach that utilizes blockchain and software defined networking (SDN) techniques to remove the re-authentication in repeated handover among heterogeneous cells. The proposed approach is designed to assure the low delay, appropriate for the 5G network in which users can be replaced with the least delay among heterogeneous cells using their public and private keys provided by the devised blockchain component while protecting their privacy. In our comparison between Proof-of-Work (POW)-based and network-based models, the delay of our authentication handover was shown to be less than 1ms. Also, our approach demonstrated less signaling overhead and energy consumption compared to peer models.

Abstract: UAVs are expected to be an important complementary component for 5G (and beyond) communication systems to achieve the goal of global access to the Internet for all. To fully exploit the benefits of the distinct features of various UAVs, this article proposes a novel hierarchical network architecture enabled by software defined networking, which integrates cross-layer high and low altitude platforms into conventional terrestrial cellular networks to inject additional capacity and expand the coverage for underserved areas in a flexible, seamless, and cost-effective manner. Specifically, we first present a comprehensive comparison and review of different types of UAVs for communication services. Then, we propose an integrated airground heterogeneous network architecture and outline its characteristics and potential advantages. Next, several key enabling techniques for the integrated system are discussed in detail. In addition, we identify the potential application scenarios where the system can further enhance the performance of traditional terrestrial networks, followed by a case study to demonstrate the effectiveness of the proposed architecture. Finally, the discussions on challenges and open research issues are given.

Abstract: The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.

Abstract: Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the diverse performance requirements among different 5G scenarios, an adaptive VNF placement approach is needed to automatically accommodate to service-specific requirements. In this paper, we tackle the VNF placement problem by first proposing a general 5G network slice framework, which jointly contains both edge cloud and core cloud servers. Specially, based on the fact that VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantity the VNF interference. With an aim to maximize the total throughput of accepted requests, we propose an Adaptive Interference-Aware (AIA) heuristic approach to automatically place VNFs in 5G service-customized network slices. Through simulations on two typical 5G scenarios, we demonstrate that AIA can efficiently handle traffic variation especially caused by VNF interference and improve the total throughput by 20.11% and 24.21% in autonomous driving and 4K/8K HD video network slices as compared with the state-of-the-art methods.

Abstract: In order to simplify the management of the traditional network, software-defined networking (SDN) has been proposed as a promising paradigm shift that decouples control plane and data plane, providing programmability to configure the network. With the deployment and the applications of SDN, researchers have found that the controller placement directly affects network performance in SDN. In this paper, the state of the art of controller placement problem is surveyed from the perspective of optimization objective. First, we introduce the overview of SDN and controller placement problem. Then, we classify this paper of controller placement problem into four aspects (latency, reliability, and cost and multi-objective) depending on their objective and analyze specific algorithms in different application scenarios. Finally, we identify some relevant open issues and research challenge to deal with in the future and conclude the controller placement problem.

Abstract: The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

Abstract: Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

Abstract: The explosive rise of intelligent devices with ubiquitous connectivity have dramatically increased Internet of Things (IoT) traffic in the cloud environment and created potential attack surfaces for cyber-attacks. Traditional security approaches are insufficient and inefficient to address security threats in cloud-based IoT networks. In this vein, software defined networking (SDN), network function virtualization (NFV), and machine learning techniques introduce numerous advantages that can effectively resolve cybersecurity matters for cloud-based IoT systems. In this paper, we propose a collaborative and intelligent network-based intrusion detection system (NIDS) architecture, namely SeArch for SDN-based cloud IoT networks. It composes a hierarchical layer of intelligent IDS nodes working in collaboration to detect anomalies and formulate policy into the SDN-based IoT gateway devices to stop malicious traffic as fast as possible. We first describe a new NIDS architecture with a comprehensive analysis in terms of the system resource and path selection optimizations. Next, the system process logic is extensively investigated through main consecutive procedures, including initialization, runtime operation, and database update. Afterward, we conduct a detailed implementation of the proposed solution in an SDN-based environment and perform a variety of experiments. Finally, evaluation results of the SeArch architecture yield outstanding performance in anomaly detection and mitigation as well as bottleneck problem handling in the SDN-based cloud IoT networks in comparison with existing solutions.

Abstract: A sophisticated and efficient network slicing architecture is needed to support the orchestration of network slices across multiple administrative domains Such multi-domain architecture shall be agnostic of the underlying virtualization and network infrastructure technologies Its objective is to extend the traditional orchestration, management and control capabilities by means of models and constructs in order to form a well-stitched composition of network slices To facilitate such a composition of networking and compute/storage resources, this article introduces a management and orchestration architecture that incorporates Software Defined Networking (SDN) and Network Function Virtualization (NFV) components to the basic 3GPP network slice management The proposed architecture is broadly divided into four major strata, namely the Multi-domain Service Conductor Stratum, Domain-specific Fully- Fledged Orchestration Stratum, Sub-Domain MANO and Connectivity Stratum, and Logical Multi-domain Slice Instance stratum Each of these strata is described in detail, providing the fundamental operational specifics for instantiating and managing the resulting federated network slices

Abstract: Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware. While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes. NFV and SDN are complementary technologies capable of providing one network solution. SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain. There are many studies designing NFV/SDN architectures in different environments. Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs. This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: (i) to investigate and provide an in-depth review of the state of the art of NFV/SDN architectures, (ii) to synthesize their architectural designs, and (iii) to identify areas for further improvements. Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state of the practice) of integrated NFV/SDN architectures and shed some light on future research efforts and the challenges faced.

Abstract: Software-defined networking (SDN) is making their way into the fifth generation of mobile communications. For example, 3GPP is embracing the concept of control-user plane separation (a cornerstone concept in SDN) in the 5G core and the radio access network (RAN). In this paper, we introduce a flexible, programmable, and open-source SDN platform for heterogeneous 5G RANs. The platform builds on an open protocol that abstracts the technology-dependent aspects of the radio access elements, allowing network programmers to deploy complex management tasks as policies on top of a programmable logically centralized controller. We implement the proposed solution as an extension to the 5G-EmPOWER platform and release the software stack (including the southbound protocol) under a permissive APACHE 2.0 license. Finally, the effectiveness of the platform is assessed through three reference use cases: 1) active network slicing; 2) mobility management; and 3) load-balancing.

Abstract: Recent advances in wireless communication, sensing, computation and control technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Demanded by the requirements of information-centric and data-driven intelligent transportation systems (ITS), it is of great significance to explore new paradigms of IoV in supporting large-scale, real-time, and reliable information services. In this article, we propose a hierarchical system architecture, which aims at synthesizing the paradigms of software defined networking and fog computing in IoV and best exploiting their synergistic effects on information services. Specifically, a four-layer architecture is designed, comprising the application layer, the control layer, the virtualization layer, and the data layer, with objectives of enabling logically centralized control via the separation of the control plane and the data plane; facilitating adaptive resource allocation and QoS oriented services based on network functions virtualization and network slicing, and enhancing system scalability, responsiveness, and reliability by exploiting the networking, computation, communication, and storage capacities of fog-based services. On this basis, we further analyze newly arising challenges and discuss future research directions by presenting a cross-layer protocol stack. Finally, for the proof of concept, we implement the system prototype and give two case studies in real-world IoV environments. The results of field tests not only demonstrate the great potential of the new architecture, but also give insight into the development of future ITS.

Abstract: In recent years, with the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the number of edge devices and the data generated from the edge have been growing rapidly. According to International Data Corporation (IDC) prediction [20] , global data will reach 180 zettabytes (ZB), and 70% of the data generated by IoT will be processed on the edge of the network by 2025. IDC also forecasts that more than 150 billion devices will be connected worldwide by 2025. In this case, the centralized processing mode based on cloud computing is not efficient enough to handle the data generated by the edge. The centralized processing model uploads all data to the cloud data center through the network and leverages its supercomputing power to solve the computing and storage problems, which enables the cloud services to create economic benefits. However, in the context of IoT, traditional cloud computing has several shortcomings.

Abstract: Computer networks have become a critical infrastructure. In fact, networks should not only meet strict requirements in terms of correctness, availability, and performance but they should also be very flexible and support fast updates, e.g., due to policy changes, increasing traffic, or failures. This paper presents a structured survey of mechanism and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of software-defined networks and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well.

Abstract: Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular “tuning knobs” in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly “self-driving” networks.

Abstract: Distributed Denial of Service attack (DDoS) is recognized to be one of the most catastrophic attacks against various digital communication entities. Software-defined networking (SDN) is an emerging technology for computer networks that uses open protocols for controlling switches and routers placed at the network edges by using specialized open programmable interfaces. In this article, a detailed study on DDoS threats prevalent in SDN is presented. First, SDN features are examined from the perspective of security, and then a discussion on SDN security features is done. Further, two viewpoints on protecting networks against DDoS attacks are presented. In the first view, SDN utilizes its abilities to secure conventional networks. In the second view, SDN may become a victim of the threat itself because of the centralized control mechanism. The main focus of this research work is on discovering critical security implications in SDN while reviewing the current ongoing research studies. By emphasizing the available state-of-the-art techniques, an extensive review of the advancement of SDN security is provided to the research and IT communities.

Abstract: The ever-increasing demands of vehicular networks pose significant challenges such as availability, computation complexity, security, trust, authentication, etc. This becomes even more complicated for high-speed moving vehicles. As a result, increasing the capacity of these networks has been attracting considerable awareness. In this regard, the next generation of cellular networks, 5G, is expected to be a promising solution enabling high data rates, capacity, and quality of service as well as low latency communications. However, 5G networks still face challenges in providing ubiquitous and reliable connections among high-speed vehicles. Thus, to overcome the gaps in the existing solutions, we propose a software defined network (SDN)-based consolidated framework providing end-to-end security and privacy in 5G enabled vehicular networks. The framework simplifies network management through SDN, while achieving optimized network communications. It operates in two phases: first, an elliptic curve cryptographic based authentication protocol is proposed to mutually authenticate the cluster heads and certificate authority in SDN-based vehicular setups, and, second, an intrusion detection module supported by tensor based dimensionality reduction is designed to reduce the computational complexity and identify the potential intrusions in the network. In order to assess the performance of the proposed framework, an extensive evaluation is performed on three simulators; NS3, SUMO, and SPAN. To harness the potential benefits of the proposed model, the first module, is evaluated on the basis of security features, whereas the second module is evaluated, and compared with the existing state-of-the-art models, on the basis of detection rate, false positive rate, accuracy, detection time, and communication overhead. The simulation results indicate the superiority of the proposed framework as compared to the existing models.