Summary
Distributed denial of service (DDoS) is a special form of denial of service attack. In this paper, a DDoS detection model and defense system based on deep learning in Software‐Defined Network ...(SDN) environment are introduced. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. By using the defense system based on the model, the DDoS attack traffic can be effectively cleaned in Software‐Defined Network. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. It also reduces the degree of dependence on environment, simplifies the real‐time update of detection system, and decreases the difficulty of upgrading or changing detection strategy.
This paper introduces a DDoS detection model and defense system based on deep learning in Software Defined Network (SDN) environment. The model can learn patterns from sequences of network traffic and trace network attack activities in a historical manner. The experimental results demonstrate the much better performance of our model compared with conventional machine learning ways. Moreover, the implemented defense system based on the model can effectively clean the DDoS attack traffic.
Cooperative Adaptive Cruise Control (CACC) is a promising technology for improving the capacity and energy efficiency of the ground transportation system. In this paper, a novel CACC control scheme ...is proposed to deal with the adverse impacts of both inter- and intra-vehicle network delays. First, a hetero-integration poly-net (PN) loop delay analysis method is presented to clarify the system delays in CACC considering both inter- and intra-vehicle network influences. A mathematic equation is put forward to calculate the upper bound of the PN loop delays. Then a collaborative software-defined network scheme is presented to deal with the PN loop delays, which consists of the application/strategy, network-control and network-data planes. In the network-control plane, a fraction-type basic period scheduling method is adopted. In the application/strategy plane, a delay-tolerant model predictive controller is designed for decision-making while a combination of an H ∞ controller and a linear quadratic regulator is adopted for acceleration tracking control against local intra-vehicle network delays. Finally, the proposed scheme is verified under a variety of scenarios based on comprehensive Hardware-in-the-Loop tests.
Unmanned aerial vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications, where human lives would otherwise be endangered. Multi-UAV ...systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of mobile ad hoc networks (MANETs), and vehicular ad hoc networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic and have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software defined networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network lead to the requirement of seamless handovers, where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute toward greening of the network. This paper surveys the work done toward all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.
Abstract
Nowadays datacenters face a big challenge, because the development of cloud services requests a large amount of capital input, high maintenance skills and cost. The major disadvantages of ...current datacenter network include the high cost of equipment and maintenance, QoS, failure recovery time, network virtualization etc. In this paper, we proposed a software defined datacenter network. The programmable controller is responsible for the management of the traffic scheduling including topology discovery, routing and Quality of Service. The SDN is based on Fat-tree network architecture and max-min fairness. A Genetic Algorithm optimized Radial Basis Function neural network is used to compute the service weight for maximizing the utilization of network resource. An experiment of the network bearing different kinds of services based on Openflow is described. This well-designed fat-tree network has high efficiency in traffic distribution, and has integrated the traditional network architecture and new SDN technology.
Abstract
For intelligent control of network traffic, to solve the problem of low utilization of public network bandwidth, we put forward scheduling data flow between data center based on SDN ...technology. First, we connect the data center distributed in different locations by wide area network and exchange or share the research data of high performance. Then, the system can sense the utilization and performance of the network bandwidth resources between the data center. Based on the results of perception, we can dynamically adjust and optimize the path of data and maximize the utilization of network link. SDN is a new software based network structure. This paper uses SDN network architecture to schedule the traffic of data center, and constructs a two-layer network suitable for cloud computing and virtualization environment. It realizes the global, dynamic and intelligent data flow.
Energy Internet (EI) is developing and booming rapidly with the increase of distributed energy resources, which is beneficial to address the severe condition of industrial energy. However, there are ...inevitable credit crises and utility optimization challenges in EI that need to be settled. In this paper, we propose a blockchain-assisted software defined energy internet (BSDEI), where a distributed energy market smart contract is designed to ensure transactions executed reliably and participants' accounts dealt accurately. In order to jointly optimize the utilities of operators, retailers and industrial prosumers in BSDEI, we formulate the whole trading process as a three-stage Stackelberg game, with the proof of existence and uniqueness for the Stackelberg equilibrium. Then, we design a hierarchical distributed policy gradient (HDPG) algorithm to solve the Stackelberg game under incomplete information. We implement a blockchain-based industrial energy trading system using a middleware platform. The smart contract is deployed on the consortium blockchain, providing website interfaces for participants to operate. Furthermore, we conduct experiments for analyzing economic benefits. Our system prototype demonstrates the feasibility of BSDEI and the algorithm exceeds about 18% in total mean reward than comparing algorithms.
throughput for high-resolution remote video surveillance. 5G cellular network as today's most advanced wireless technology will be the perfect match for Agriculture 4.0 requirements. In its maturity ...process, the 5G network requires various optimizations, one of which is by making route algorithm calculation modifications in terms of determining the best route for a data packet from a data source to a data destination. To achieve this goal, it requires research in the form of experiments using network simulator. Software Define Network (SDN) as network programmability is used to modify route in Dijkstra algorithm calculation, and run several use case that simulate 5G network characteristic. By adding bandwidth utilization and latency parameters into the routing algorithm calculations, 5G requirements such as packet loss below 1% and latency below 5ms are successfully achieved. These positive results may be further tested on real 5G networks, if in the future this research also gets positive results in testing on a real 5G network, then cellular network customers will be able to experience an increase in service quality.
A software-defined network (SDN) separates the network control plane from the data forwarding plane. SDN has shown significant benefits in many ways compared to conventional non-SDN networks. ...However, traffic distribution in SDN impacts efficiency and raises many other challenges. For instance, uneven load distribution in the SDN significantly impacts the network performance. Hence, several SDN load balancing (LB) techniques have been introduced to improve the efficiency of SDN. In this article, we provide a thematic taxonomy of LB in SDN, considering several parameters from the past technical studies such as the objectives of LB, data plane LB techniques, control plane LB techniques, other aspects of data plane/control plane LB as well as the performance metrics for LB techniques. Furthermore, useful insights on LB and a comparative analysis of various promising SDN LB techniques are also included in the survey. Finally, existing challenges and future direction on SDN LB techniques are highlighted.
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•A comprehensive literature review of pertinent studies on Load Balancing in Software Defined Networks.•Thematic taxonomy for classifying current load balancing techniques in SDN.•Lesson learned from in-depth critical review based on proposed taxonomy.•Key future direction is discussed based on the SDN requirement.
Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for the next-generation Internet. The deployment and applications ...of IoT range from smart cities to urban computing and from ubiquitous healthcare to tactile Internet. For this reason, the physical infrastructure of heterogeneous network systems has become more complicated and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks and Network Function Virtualization has been extensively researched for IoT in the recent past. In this article, we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software-defined networks designed for IoT, function virtualization for IoT networks, and software-defined IoT networks. These categories are further divided into works that present architectural, security, and management solutions. Besides, the article highlights several short-term and long-term research challenges and open issues related to the adoption of software-defined Internet of Things.
The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to ...support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.
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