Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Edge computing oriented virtual optical network mapping scheme based on fragmentation prediction

As edge computing services soar,the problem of resource fragmentation situation is greatly worsened in elastic optical networks(EON).Aimed to solve this problem,this article proposes the fragmentation prediction model that makes full use of the gate recurrent unit(GRU)algorithm.Based on the fragmentation prediction model,one virtual optical network mapping scheme is presented for edge computing driven EON.With the minimum of fragmentation degree all over the whole EON,the virtual network mapping can be successively conducted.Test results show that the proposed approach can reduce blocking rate,and the supporting ability for virtual optical network services is greatly improved.

A highly reliable embedding algorithm for airborne tactical network virtualization

The evolution of airborne tactical networks(ATNs)is impeded by the network ossification problem.As a solution,network virtualization(NV)can provide a flexible and scalable architecture where virtual network embedding(VNE)is a key part.However,existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in aircombat field.In this context,a highly reliable VNE algorithm based on the transmission rate for ATN virtualization(TR-ATVNE)is proposed to adapt well to the specific electromagnetic environment of ATN.Our algorithm coordinates node and link mapping.In the node mapping,transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks.Meanwhile,a feasible splitting rule is proposed for path splitting in the link mapping,considering the interference between wireless links.Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

ApplianceBricks: A Scalable Network Appliance Architecture for Network Functions Virtualization

Network Functions Virtualization(NFV) is an attempt to help operators more effectively manage their networks by implementing traditional network functions embedded in specialized hardware platforms in term of virtualized software instances. But, existing novel network appliances designed for NFV infrastructure are always architected on a general-purpose x86 server, which makes the performance of network functions limited by the hosted single server. To address this challenge, we propose ApplianceB ricks, a novel NFV-enable network appliance architecture that is used to explore the way of consolidating multiple physical network functions into a clustered network appliance, which is able to improve the processing capability of NFV-enabled network appliances.

A dynamic and resource sharing virtual network mapping algorithm

Network virtualization can effectively establish dedicated virtual networks to implement various network functions.However,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and priority.In addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation redundancy.Based on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology properties.In the node mapping process,three properties of the node are used to measure its mapping ability.Second,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link mapping.Finally,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource allocation.The former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision probability.Simulation results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenue by 15%and 38%,and reduce the network cost and link pressure by 25%and 17%.

Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network congestions.The setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge softwarization.To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth capacities.Adaptive partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop ratio.The optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation steps.The reward formulation primarily considers taskrequired computing resources and action-applied allocation properties.With defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated properties.The simulation for the control rule installation is conducted using Mininet and Ryu SDN controller.Average delay and task delivery/drop ratios are used as the key performance metrics.

Virtualization Technology in Cloud Computing Based Radio Access Networks:A Primer

Since virtualization technology enables the abstraction and sharing of resources in a flexible management way, the overall expenses of network deployment can be significantly reduced. Therefore, the technology has been widely applied in the core network. With the tremendous growth in mobile traffic and services, it is natural to extend virtualization technology to the cloud computing based radio access networks(CCRANs) for achieving high spectral efficiency with low cost.In this paper, the virtualization technologies in CC-RANs are surveyed, including the system architecture, key enabling techniques, challenges, and open issues. The enabling key technologies for virtualization in CC-RANs mainly including virtual resource allocation, radio access network(RAN) slicing, mobility management, and social-awareness have been comprehensively surveyed to satisfy the isolation, customization and high-efficiency utilization of radio resources. The challenges and open issues mainly focus on virtualization levels for CC-RANs, signaling design for CC-RAN virtualization, performance analysis for CC-RAN virtualization, and network security for virtualized CC-RANs.

Real-Time Timing Channel Detection in a Software-Defined Networking Virtual Environment

Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.

A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization

5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.

Service-Oriented Network Virtualization Architecture for Internet of Things

As a key technology to realize smart services of Internet of Things(Io T),network virtualization technology can support the network diversification and ubiquity,and improve the utilization rate of network resources.This paper studies the service-oriented network virtualization architecture for Io T services.Firstly the semantic description method for Io T services is proposed,then the resource representation model and resource management model in the environment of network virtualization are presented.Based on the above models,the service-oriented virtual network architecture for Io T is established.Finally,a smart campus system is designed and deployed based on the service-oriented virtual network architecture.Moreover,the proposed architecture and models are verified in experiments.

Network Virtualization Based Seamless Networking Scheme for Fiber-Wireless （FiWi） Networks

In order to reduce cost and complexity,fiber-wireless(FiWi) networks emerge,combining the huge amount of available bandwidth of fiber networks and the flexibility,mobility of wireless networks.However,there is still a long way to go before taking fiber and wireless systems as fully integrated networks.In this paper,we propose a network visualization based seamless networking scheme for FiWi networks,including hierarchical model,service model,service implementation and dynamic bandwidth assignment(DBA).Then,we evaluate the performance changes after network virtualization is introduced.Throughput for nodes,bandwidth for links and overheads leaded by network virtualization are analyzed.The performance of our proposed networking scheme is evaluated by simulation and real implementations,respectively.The results show that,compared to traditional networking scheme,our scheme has a better performance.

Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Virtual network mapping algorithm for large-scale network environment

A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.

The Network Architecture and Protocols for Distributed Virtual Environment

In this paper, the properties of distributed virtual environment (DVE) and the requirements on computer networks is briefly reviewed. A multicast protocol, called sender initiated grouping multicast protocol for DVE (SIGMP), is proposed. This new multicast protocol is based on a novel concept, multicast group (MG), which divides all participants in a DVE system into groups, among which there is a multicast group trustee (MGT) node to manage the group. The protocol provides unreliable/reliable, totally ordered and multiple to multiple multicast transmission service for DVE systems without sacrificing the communication efficiency heavily. At the same time, reliable unicast and one to multiple multicast transmission services are also supported. The performance analysis of the new protocols is also presented. Based on SIGMP, a simple demonstration of DVE system is designed and implemented. This demo system is running on several SGI workstations connected by a FDDI and Ethernet network.

Network Function Virtualization Technology:Progress and Standardization

Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/CT integration. Network function virtualization （NFV） may inspire new development ideas, but many doubts still exist within industry, especially about how to introduce NFV into an operator＇ s network. This article describes the latest progress in NFV standardization, NFV requirements and hot technology issues, and typical NFV applications in an operator networks.

QoE Assessment of Will Transmission Using Vision and Haptics in Networked Virtual Environment

In this paper, we handle collaborative work in which two users move an object together to eliminate a target in a 3-D virtual space. In the work, the users transmit their wills about movement direction of the object to each other by only haptics and by haptics and vision (including with/ without drawing an arrow to indicate the direction of force applied to the object by the other user). We carry out QoE (Quality of Experience) assessment subjectively and objectively to investigate the influence of network delay on will transmission. As a result, we clarify the effects of vision on the transmissibility of haptic will transmission.

Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm

In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.

Convolutional neural networks for time series classification

Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging problem due to the nature of time series data: high dimensionality, large in data size and updating continuously. The deep learning techniques are explored to improve the performance of traditional feature-based approaches. Specifically, a novel convolutional neural network (CNN) framework is proposed for time series classification. Different from other feature-based classification approaches, CNN can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains. The final experimental results show that the proposed method outperforms state-of-the-art methods for time series classification in terms of the classification accuracy and noise tolerance. © 1990-2011 Beijing Institute of Aerospace Information.