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

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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A Combination of rNN and CNN for Attention-based relation Classification

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Procedia Computer Science 2018年 131卷 911-917页

作者： Xiaobin Zhang Fucai Chen ruiyang Huang National Digital Switching System Engineering & Techological R&D center ZhengZhou 450002 China

relation classification plays an important role in the field of natural language processing (NLP). Previous research on relation classification has verified the effectiveness of using convolutional neural network (CNN) and recurrent neural network (rNN). In this paper, we proposed a model that combine the rNN and CNN (rCNN), which will Give full play to their respective advantages: rNN can learn temporal and context features, especially long-term dependency between two entities, while CNN is capable of catching more potential features. We experiment our model on the SemEval-2010 Task 8 dataset 1 , and the result shows that our method is superior to most of the existing methods.

关键词： relation classification CNN rNN Word2Vec part-of-speech (POS) tags

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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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Tailoring the Electronic Metal-Support Interactions in Supported Silver Catalysts through Al modification for Efficient Ethylene Epoxidation

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Angewandte Chemie 2024年第16期136卷

作者： dr. Hongling Yang dr. Ganggang Li dr. Qinggang Liu dr. Haixia Cheng dr. Xiaoxu Wang Prof. Jie Cheng dr. Guoxia Jiang dr. Fenglian Zhang Prof. Zhongshen Zhang Prof. Zhengping Hao National Engineering Laboratory for VOCs Pollution Control Material & Technology Research Center for Environmental Material and Pollution Control Technology University of the Chinese Academy of Sciences Beijing 101408 China Beijing Key Laboratory for VOCs Pollution Prevention and Treatment Technology and Application of Urban Air Beijing Municipal Research Institute of Eco-Environmental Protection Beijing 100037 China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China Material Digital R&D Center China Iron & Steel Research Institute Group Beijing 100081 China DP Technology Beijing 100080 China

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Extracellular polymeric substance-intercalated MXene membrane for osmotic power generation

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Chemical engineering Journal 2025年 515卷

作者： Cao, da-Qi Tang, Kai Yihuo, Guri Jin, Yan Song, Yi-Xuan Wu, Yun-Feng Zhang, Wen-Yu Zhang, Zhongguo Ma, Ming-Guo Song, Li Hao, Xiao-di Sino-Dutch R&D Centre for Future Wastewater Treatment Technologies/Key Laboratory of Urban Stormwater System and Water Environment Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Municipal Research Institute of Eco-Environmental Protection Beijing100037 China National Engineering Laboratory of Circular Economy Institute of Resources and Environment Beijing Academy of Science and Technology Beijing100095 China Research Center of Biomass Clean Utilization MOE Engineering Research Center of Forestry Biomass Materials and Bioenergy Beijing Key Laboratory of Lignocellulosic Chemistry College of Materials Science and Technology Beijing Forestry University Beijing100083 China National Synchrotron Radiation Laboratory Chinese Academy of Sciences Center for Excellence in Nanoscience University of Science and Technology of China Anhui Hefei230029 China

Extracellular polymeric substances (EPSs) recycled from excess sludge in wastewater treatment plant are biological macromolecules and high value-added materials, because of containing abundant characteristic functional groups. In this work, using EPSs as an intercalation agent between two-dimensional (2d) transition metal carbide and nitride (MXene) nanosheets, EPS-intercalated MXene (EPS-M) membranes were successfully constructed for reverse electrodialysis (rEd) salinity gradient energy harvesting. The interlayer spacing of MXene nanosheets in the EPS-M membranes was constrained effectively at 1.48 ± 0.02 nm regardless of environmental conditions. For the optimized EPS-M membrane with 10 % EPS mass content, the ultimate strength was 71 MPa, which was 6 times higher than that obtained by pristine MXene membrane. The typical groups such as hydroxyl and carboxyl groups present in EPS enhanced the negative surface charge of EPS-M membranes, resulting in high ion permeability and cation selectivity. The enhanced hydrophilicity induced by EPS contributed to the reduced membrane resistance. A model (0.5 M/0.01 M NaCl solutions) was used to investigate the salinity gradient power generation of sea water/secondary effluent of WWTP in rEd process with EPS-M membrane;and the maximum output power density reached 5.89 W m−2, which was 1.86 times higher than that obtained using the pristine MXene membrane. This study opens a novel application of high value-added recycled EPS, which can be considered as a cost-effective biological composite material to improve MXene 2d membrane performance compared to the macromolecules such as nanofiber and cellulose. © 2025 Elsevier B.V.

关键词： Wastewater reclamation

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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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