2020 IEEE 6th International Conference on Computer and Communications (ICCC)最新文献_第8页

A Method for Constructing Heterogeneous Entities Pool in NFV Security Architecture Based on Mimic Defense 基于模拟防御的NFV安全体系结构异构实体池构建方法

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345155

Qingqing Zhang, Hongbo Tang, Wei You, Yingle Li

引用次数: 1

An Improved Deep Supervised Hashing Method for Hamming Space Retrieval 一种改进的深度监督哈希法用于汉明空间检索

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345161

Xiangdong Lin, W. Zou, Nan Hu, Jiajun Wang

引用次数: 0

Compressive-Sensing-Based Antenna Array Calibration With Manifold Separation Technique 基于压缩传感的流形分离技术天线阵列标定

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345107

Tianhan Tan, Daolin Chen, Yisong Xue, Jie Zhuang

引用次数: 1

Deep Reinforcement Learning Based Computing Offloading and Resource Allocation Algorithm for Mobile Edge Networks 基于深度强化学习的移动边缘网络计算卸载与资源分配算法

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345089

Jinwei Xu, Xu Liu, Xiaorong Zhu

引用次数: 0

A Communication Model to Enhance Industrial Wireless Networks based on Time-Sensitive Networks 一种基于时间敏感网络增强工业无线网络的通信模型

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345100

Yue Li, Yue Ma, Zhenyu Yin, Ai Gu, Fulong Xu

引用次数: 3

An Improved Algorithm for Adaptive Communication Frame Length Based on Modbus Protocol 一种基于Modbus协议的自适应通信帧长的改进算法

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345057

Yao Yuanyuan, Chen Meng

引用次数: 1

A Two-Layer Moving Target Defense for Image Classification in Adversarial Environment 对抗环境下图像分类的两层运动目标防御

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345217

Ye Peng, Guobin Fu, Yingguang Luo, Qi Yu, Bin Li, Jia Hu

{"title":"A Two-Layer Moving Target Defense for Image Classification in Adversarial Environment","authors":"Ye Peng, Guobin Fu, Yingguang Luo, Qi Yu, Bin Li, Jia Hu","doi":"10.1109/ICCC51575.2020.9345217","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345217","url":null,"abstract":"Deep learning plays an increasingly important role in various fields due to its superior performance, and it also achieves advanced recognition performance in the field of image classification. However, the vulnerability of deep learning in the adversarial environment cannot be ignored, and the prediction result of the model is likely to be affected by the small perturbations added to the samples by the adversary. In this paper, we propose a two-layer dynamic defense method based on defensive techniques pool and retrained branch model pool. First, we randomly select defense methods from the defense pool to process the input. The perturbation ability of the adversarial samples preprocessed by different defense methods changed, which would produce different classification results. In addition, we conduct adversarial training based on the original model and dynamically generate multiple branch models. The classification results of these branch models for the same adversarial sample is inconsistent. We can detect the adversarial samples by using the inconsistencies in the output results of the two layers. The experimental results show that the two-layer dynamic defense method we designed achieves a good defense effect.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114207361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Flexible Deployment Scheme for Virtual Network Function Based on Reinforcement Learning 一种基于强化学习的虚拟网络功能灵活部署方案

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9344881

J. Yao, Meijuan Chen

{"title":"A Flexible Deployment Scheme for Virtual Network Function Based on Reinforcement Learning","authors":"J. Yao, Meijuan Chen","doi":"10.1109/ICCC51575.2020.9344881","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344881","url":null,"abstract":"Network function virtualization (NFV) technology is widely used in network slicing in 5G networks and traffic processing in wide area network. However, with the increasing of service requests during the life cycle of a virtual network function (VNF), how to flexibly deploy the VNF becomes a key problem to make maximum use of the limited capacity of the physical network resources, and meanwhile satisfy the requirements of quality of service (QoS) in NFV scenario. In this paper, aiming to solve whether and how to scale VNF on demand, we formulated this problem as a non-convex linear mathematical optimization model where the optimization goal is to minimize the delay and energy consumption of the service function chain (SFC). Specifically, we propose a VNF flexible deployment scheme based on Reinforcement Learning (RL). Moreover, we train the agent by interacting with the physical network environment and take action according to the state of physical node to find the optimal physical resource allocation strategy of the VNF scaling. In addition, the state space, action space and the reward function are defined as available resource, migration or scaling decision and the reciprocal of total cost respectively. Extensive simulation results demonstrate that the proposed algorithm outperforms the comparison algorithm in terms of reducing the delay and increasing the ratio of successful scaling request.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Artificial Intelligence Security Issues and Responses 人工智能安全问题与应对

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9345035

Haobo Zhao, Liquan Chen

引用次数: 0

Node Location and Route Optimization of a Two-layer Underground Logistics Network 双层地下物流网络节点定位与路径优化

2020 IEEE 6th International Conference on Computer and Communications (ICCC) Pub Date : 2020-12-11 DOI: 10.1109/ICCC51575.2020.9344930

Xia Liu, Nan Li

引用次数: 1