2019 International Conference on Computing, Networking and Communications (ICNC)最新文献_第4页

Privacy-Preserving Device Discovery and Authentication Scheme for D2D Communication in 3GPP 5G HetNet 3GPP 5G网络中D2D通信的隐私保护设备发现和认证方案

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685499

Yunqing Sun, Jin Cao, M. Ma, Hui Li, Ben Niu, Fenghua Li

{"title":"Privacy-Preserving Device Discovery and Authentication Scheme for D2D Communication in 3GPP 5G HetNet","authors":"Yunqing Sun, Jin Cao, M. Ma, Hui Li, Ben Niu, Fenghua Li","doi":"10.1109/ICCNC.2019.8685499","DOIUrl":"https://doi.org/10.1109/ICCNC.2019.8685499","url":null,"abstract":"Device-to-device (D2D) communication as a direct communication technology has many application scenarios and plays a very important role in the 5G era. Using D2D communication in 3GPP 5G Heterogeneous Network (HetNet) can effectively relieve the network traffic pressure and reduce the energy consumption of the base station. However, there are numerous security threats in D2D application since the introduction of D2D communication into the 3GPP 5G Het-Net still remains in the early stage. It is urgent to design a mutual authentication and key agreement protocol between two heterogeneous User Equipments (UEs) with privacy preserving and device discovery. The existing standards and solutions rarely take the heterogeneous access scenarios, privacy protection, and device discovery into consideration. In this paper, we propose a unified privacy protection device discovery and authentication mechanism for heterogeneous D2D UEs using the identity-based prefix encryption and ECDH techniques. Our proposed scheme can be applied to all of the 5G heterogeneous access scenarios of D2D communication. The security analysis and performance results show that our scheme can achieve the mutual authentication, key agreement, identity privacy protection and resist several protocols attacks with ideal efficiency.","PeriodicalId":161815,"journal":{"name":"2019 International Conference on Computing, Networking and Communications (ICNC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124537239","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: 26

MongoDB Clustering using K-means for Real-Time Song Recognition 基于K-means的MongoDB聚类实时歌曲识别

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685489

Murtadha Arif Bin Sahbudin, M. Scarpa, Salvatore Serrano

引用次数: 4

Blocking Probability Analysis for Relay-Assisted OFDMA Networks using Stochastic Geometry 基于随机几何的中继辅助OFDMA网络阻塞概率分析

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685498

A. Alahmadi, Yuan Liang, Run Tian, Jian Ren, Tongtong Li

引用次数: 2

Distributed Multi-Stream Beamforming in Multi-Relay Interference Networks with Multi-Antenna Nodes 多天线节点多中继干扰网络中的分布式多流波束形成

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685603

C. M. Yetis, Ronald Y. Chang

引用次数: 0

Hadoop Performance Evaluation in Software Defined Data Center Networks 软件定义数据中心网络中的Hadoop性能评估

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685506

Claudio Calcaterra, A. Carmenini, A. Marotta, D. Cassioli

引用次数: 3

Clustering Enabled Classification using Ensemble Feature Selection for Intrusion Detection 基于集成特征选择的入侵检测聚类分类

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685636

F. Salo, M. Injadat, Abdallah Moubayed, A. B. Nassif, A. Essex

{"title":"Clustering Enabled Classification using Ensemble Feature Selection for Intrusion Detection","authors":"F. Salo, M. Injadat, Abdallah Moubayed, A. B. Nassif, A. Essex","doi":"10.1109/ICCNC.2019.8685636","DOIUrl":"https://doi.org/10.1109/ICCNC.2019.8685636","url":null,"abstract":"Machine learning has been leveraged to increase the effectiveness of intrusion detection systems (IDSs). The focus of this approach, however, has largely be on detecting known attack patterns based on outdated datasets. In this paper, we propose an ensemble feature selection method along with an anomaly detection method that combines unsupervised and supervised machine learning techniques to classify network traffic to identify previously unseen attack patterns. To that end, three different feature selection techniques are used as part of an ensemble model that selects 8 common features. Moreover, k-Means clustering is used to first partition the training instances into k clusters using the Manhattan distance. A classification model is then built based on the resulting clusters, which represent a density region of normal or anomaly instances. This in turn helps determine the effectiveness of the clustering in detecting unknown attack patterns within the data. The performance of our classifier is evaluated using the Kyoto dataset, which was collected between 2006 and 2015. To our knowledge, no previous work proposed such a framework that combines unsupervised and supervised machine learning approaches using this dataset. Experimental results show the effectiveness of the proposed framework in detecting previously unseen attack patterns compared to the traditional classification approach.","PeriodicalId":161815,"journal":{"name":"2019 International Conference on Computing, Networking and Communications (ICNC)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130407399","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: 32

Mobile Device Localization in 5G Wireless Networks 5G无线网络中的移动设备定位

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685597

Dandan Wang, Gurudutt Hosangadi, Pantelis Monogioudis, A. Rao

{"title":"Mobile Device Localization in 5G Wireless Networks","authors":"Dandan Wang, Gurudutt Hosangadi, Pantelis Monogioudis, A. Rao","doi":"10.1109/ICCNC.2019.8685597","DOIUrl":"https://doi.org/10.1109/ICCNC.2019.8685597","url":null,"abstract":"As wireless networks are evolving into 5G, tremendous amount of data will be shared on the newly developed open source platforms. These data can be used in developing new services. Among which, location information of mobile devices are extremely useful. For example, the location information can be used to assist wireless operators to trouble shoot the network performance. It can also be used to provide some location assisted service. However, some of these devices may be designed for limited budget that do not have the capability of GPS. Furthermore, operators may not have access to the GPS information on the mobile devices. In this paper, we propose a novel machine learning based approach to estimate the location of the mobile devices based on the measurement data that mobiles reported during every call and session. Our proposed algorithm utilizes the advanced features of 5G wireless network, such as the beam information. Simulation shows that the proposed solution can achieve 4m accuracy for LoS enviorment and 12m accuracy for mixed LoS and NLoS environment. And the proposed algorithm can also work even with only the information from one base station.","PeriodicalId":161815,"journal":{"name":"2019 International Conference on Computing, Networking and Communications (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130453990","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: 6

A Robust Road Region of Interest Identification Scheme for Traffic-Video Data Mining 一种用于交通视频数据挖掘的鲁棒道路兴趣区域识别方案

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685513

Anes Madani, Suman Kumar, Linh Ba Nguyen, Jiling Zhong

引用次数: 0

What To Do First: Ranking The Mission Impact Graph for Effective Mission Assurance 首先要做的是:为有效的任务保证排列任务影响图

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685579

Pranavi Appana, Xiaoyan Sun, Yuan Cheng

引用次数: 1

Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems 大数据处理系统中并行作业的智能调度

2019 International Conference on Computing, Networking and Communications (ICNC) Pub Date : 2019-02-01 DOI: 10.1109/ICCNC.2019.8685520

Mingrui Xu, C. Wu, Aiqin Hou, Yongqiang Wang

引用次数: 8