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Orchestrating Network Functions in Software-defined Networks

China Communications 2017年第2期14卷 104-117页

作者： Hongchao Hu Lin Pang Zhenpeng Wang Guozhen Cheng National Digital Switching System Engineering & Technological R&D Center Zhengzhou 450002 China

*** networking(SdN) enables *** companies to participate in the network function innovations. A number of instances for one network function will inevitably *** in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.

关键词： software-defined network network function function orchestrating

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Toward a Scalable SdN Control Mechanism via Switch Migration

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China Communications 2017年第1期14卷 111-123页

作者： Guozhen Cheng Hongchang Chen Hongchao Hu Zhiming Wang National Digital Switching System Engineering & Technological R&D Center Zhengzhou 450002 China

dynamic Controller Provisioning Problem(dCPP) is a key problem for scalable SdN. Previously, the solution to this problem focused on adapting the number of controllers and their locations with changing network conditions, but ignored balancing control loads via switch migration. In this paper, we study a scalable control mechanism to decide which switch and where it should be migrated for more balanced control plane, and we define it as Switch Migration Problem(SMP). The main contributions of this paper are as follows. First, we define a SdN model to describe the relation between controllers and switches from the view of loads. Based on this model, we form SMP as a Network Utility Maximization(NUM) problem with the objective of serving more requests under available control resources. Second, we design a synthesizing distributed algorithm for SMP--- distributed Hopping Algorithm(dHA), by approximating our optimal objective via Log-Sum-Exp function. In dHA, individual controller performs algorithmic procedure independently. With the solution space F, we prove that the optimal gap caused by approximation is at most 1/βlog|F|, and dHA procedure is equal to implementation of a time-reversible Markov Chain process. Finally, the results are corroborated by several numerical simulations.

关键词： software-defined networking scalability switch migrations Markov chain

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Towards a dynamic Controller Scheduling-Timing Problem in Software-defined Networking

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China Communications 2017年第10期14卷 26-38页

作者： Zhenping Lu Fucai Chen Guozhen Cheng Chao Qi Jianjian Ai National Digital Switching System Engineering & Technological R&D Center Zhengzhou 450002 China

Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.

关键词： software-defined networking network security controller

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Secret key generation based on private pilot under man-in-the-middle attack

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Science China(Information Sciences) 2017年第10期60卷 93-106页

作者： Yu HUANG Liang JIN Na LI Zhou ZHONG Xiaoming XU National Digital Switching System Engineering and Technological R&D Center National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications

Given the openness and invariance of public pilot, secret key generation(SKG) based on wireless channels is vulnerable to active attacks. In this paper, we explore man-in-the-middle(MITM) attacks, where the attacker acts as a transparent relay to intercept channel state information and deduce the generated *** prevent this type of attacks, a dynamic private pilot is generated, where legitimate nodes first consider the information authenticated between them as seed information for the private pilot, and then generate the private pilot based on this seed information according to the pilot requirements. Then, both the new seed information and secret keys are dynamicaally derived from wireless channels that are estimated with the private pilot instead of a public pilot. The proposed private pilot encrypts and authenticates wireless channels, allowing an SKG rate close to that without attackers. Analysis and simulation results show that the proposed SKG approach can effectively withstand an MITM attack.

关键词： private pilot man-in-the-middle attack secret key generation physical layer security

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QoS-aware Traffic Classification Architecture Using Machine Learning and deep Packet Inspection in SdNs

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Procedia Computer Science 2018年 131卷 1209-1216页

作者： Changhe Yu Julong Lan JiChao Xie Yuxiang Hu National Digital Switching System Engineering & Technological R&D Center Zhengzhou 450002 China

The QoS-aware traffic classification techniques of SdN networks is the basis for network to provide fine-grained QoS traffic engineering. In this paper, we propose an architecture which combines deep packet detection and semi-supervised machine learning of multi-classifier in SdN. This architecture can classify flows into different QoS categories. Based on this, network can achieve fine-grained adaptive QoS traffic engineering. Moreover, through deep packet detection techniques, network can maintain a dynamic flow database. Classifier can adapt to the rapid emergence of network application and fickle traffic characteristics of current network by periodically re-training with the dynamic flow database. Experiments verify that our classification framework can achieve good classification accuracy.

关键词： traffic classification QoS machine learning SdN

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Survey on quantitative evaluations of moving target defense

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网络与信息安全学报 2018年第9期4卷 66-76页

作者： Huanruo LI Yunfei GUO Shumin HUO Guozhen CHENG Wenyan LIU National Digital Switching System Engineering & Technological R&D Center Zhengzhou 450002 China

Quantitative evaluations are of great importance in network security *** recent years,moving target defense(MTd)has appeared to be a promising defense approach that blocks asymmetrical advantage of attackers and favors the defender-notwithstanding,it has a limited deployment due to its uncertain efficiency and effectiveness in *** that case,quantitative metrics and evaluations of MTd are essential to prove its capability and impulse its further *** article presents a comprehensive survey on state-of-the-art quantitative ***,taxonomy of MTd techniques is stated according to the software stack ***,a concrete review and comparison on existing quantitative evaluations of MTd is ***,notice-worthy open issues regarding this topic are proposed along with the conclusions of previous studies.

关键词：计算机网络网络管理应用程序信息安全

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Artificial Noise Aided Polar Codes for Physical LayerSecurity

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China Communications 2017年第12期14卷 15-24页

作者： Huiqing Bai Liang Jin Ming Yi China National Digital Switching System Engineering and Technological R&D Center Zhengzhou 450002 China

The secrecy rates of the existing practical secrecy coding methods are relative low to satisfy the security requirement of 5 G *** propose an artificial noise(AN) aided polar coding algorithm to improve the secrecy ***,a secrecy coding model based on AN is presented,where the confidential bits of last transmission code block are adopted as AN to inject into the current *** this way,the AN can only be eliminated from the jammed codeword by the legitimate *** the AN is shorter than the codeword,we then develop a suboptimal jamming positions selecting algorithm with the goal of maximizing the bit error rate of the *** and simulation results demonstrate that the proposed algorithm outperforms the random selection method and the method without AN.

关键词： physical layer security polarcodes artificial noise jamming positions se-lection

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Person re-Identification based on data Prior distribution

Person Re-Identification based on Data Prior Distribution

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Yancheng Wu Hongchang Chen Shaomei Li Chao Gao Hongxin Zhi Yuchao Jiang Yanchuan Wang National Digital Switching System Engineering & Technological R&D Center

In order to solve the problem of insufficient accuracy of the existing person re-identification *** propose a neural network model for identifying pedestrian properties and pedestrian Id. Compared with the existing methods, the model mainly has the following three advantages. First, our network adds extra full connection layer, ensure model migration ability. Second,based on the number of samples in each attribute, the loss function of each attribute has been normalized, avoid number unbalanced among the attributes to effect the identification accuracy. Third, we use the distribution of the attribute data in the prior knowledge, through the number to adjust the weight of each attribute in the loss layer, avoid the number of data sets for each attribute of positive and negative samples uneven impact on recognition. Experimental results show that the algorithm proposed in this paper has high recognition rate, and the rank-1 accuracy rate on dukeMTMC dataset is 72.83%,especially on Market1501 dataset. The rank-1 accuracy rate is up to 86.90%.

关键词： person re-identification data prior distribution weight adjustment deep learning neural network

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Context-based Synthetic data for Logo recognition

Context-based Synthetic Data for Logo Recognition

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2018 3rd International Conference on Computer Science and Information engineering(ICCSIE 2018)

作者： Yuchao Jiang Lixin Ji Chao Gao Yancheng Wu National Digital Switching System Engineering & Technological R&D Center

In order to solve the problem of sparse training samples in logo recognition task,a multi-type context-based logo data synthesis algorithm is *** algorithm comprehensively utilizes the local and full context of the logo object and the scene image to guide the synthesis of the logo *** experimental results on the FlickrLogos-32 show that the proposed algorithm can greatly improve the performance of the logo recognition algorithm without relying on additional manual annotation,verify the validity of the synthesis algorithm,and further prove that multi-type context can improve the performance of the object recognition algorithm.

关键词： logo recognition context data synthesis deep learning

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An Adaptation Method in Noise Mismatch Conditions for dNN-Based Speech Enhancement

An Adaptation Method in Noise Mismatch Conditions for DNN-Ba...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Siying Xu dan Qu Xingyan Long China National Digital Switching System Engineering & Technological R&D Center

ISBN: (纸本)9781510871076

The deep learning-based speech enhancement has shown considerable ***,it still suffers performance degradation under mismatch *** this paper,an adaptation method is proposed to improve the performance under noise mismatch ***,we advise a noise aware training by supplying identity vectors(ivectors) as parallel input features to adapt dNN acoustic models with the target ***,given a small amount adaptation data,the noise-dependent dNN is obtained by using Euclidean distance regularization from a noiseindependent dNN,and forcing the estimated masks to be close to the unadapted ***,experiments were carried out on different noise and SNr conditions,and the proposed method has achieved significantly 29% benefits of STOI at most and provided consistent improvement in PESQ and seg SNr against the baseline systems.

关键词： Enhancement adaptation method dNN PESQ segSNr STOI

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