MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)最新文献_第4页

An investigation into the use of software defined networking controllers in aerial networks 软件定义网络控制器在空中网络中的应用研究

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170741

D. Anderson

引用次数: 7

Online learning with side information 在线学习附带信息

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170860

Xiao Xu, Sattar Vakili, Qing Zhao, A. Swami

引用次数: 6

Joint network coding and backpressure algorithm for cognitive radio networks 认知无线网络的联合网络编码与背压算法

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170819

S. Soltani, Y. Sagduyu, Sean Scanlon, Yi Shi, Jason H. Li, Jared Feldman, J. Matyjas

{"title":"Joint network coding and backpressure algorithm for cognitive radio networks","authors":"S. Soltani, Y. Sagduyu, Sean Scanlon, Yi Shi, Jason H. Li, Jared Feldman, J. Matyjas","doi":"10.1109/MILCOM.2017.8170819","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170819","url":null,"abstract":"This paper presents the joint design of network coding and backpressure algorithm for cognitive radio networks and its implementation with software-defined radios (SDRs) in a high fidelity network emulation testbed. The backpressure algorithm is known to provide throughput optimal solutions to joint routing and scheduling for dynamic packet traffic. This solution applies to cognitive radio networks with spectrum dynamics changing over time and space, and supports joint routing and spectrum access without any need for end-to-end path maintenance. The backpressure algorithm is extended to multicast traffic with network coding deployed over virtual queues that represent different flows per session and destination. This extension is supported with different methods to decode packets at destinations. In the absence of a common control channel, distributed coordination with local information exchange is used to support neighborhood discovery, spectrum sensing and channel estimation that are integrated with joint routing, channel access and network coding. Cognitive network functionalities are implemented with GNU Radio and Python modules for different network layers, and used with USRP N210 radios. Practical radio implementation issues are addressed in a distributed wireless network setting, where USRP N210 radios communicate with each other through RFnest, a high fidelity wireless network emulation tool. RFnest provides realistic physical channel environment by digitally controlling path loss, fading, delay, topology and mobility effects. Extensive emulation test results are provided to assess throughput, backlog and energy consumption and verify the SDR implementation of joint network coding and backpressure algorithm under realistic channel and radio hardware effects.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"360 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781633","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 platform for evaluator-centric cybersecurity training and data acquisition 一个以评估人员为中心的网络安全培训和数据采集平台

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170768

Jaime C. Acosta, Joshua McKee, Alexander Fielder, S. Salamah

{"title":"A platform for evaluator-centric cybersecurity training and data acquisition","authors":"Jaime C. Acosta, Joshua McKee, Alexander Fielder, S. Salamah","doi":"10.1109/MILCOM.2017.8170768","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170768","url":null,"abstract":"Empirical-based models for security technologies in the commercial and military domain, including those that focus on protection, detection, and broader risk analysis, leverage data captured from sensors on network-connected devices including gateways, routers, and host nodes. Lacking, however, are datasets that contain specific state observations and actions from the evaluator (red/blue teammer) workstation; we call this the inside-view. This is largely due to issues associated with data ownership, data classification, and the lack of integrated evaluator-centric data-collection mechanisms. To enable and promote creation of open datasets that capture the inside-view, we introduce a scalable platform that consists of two main elements. First, the emulation sandbox, or EmuBox, is an open-source and portable (i.e., it can execute on a laptop) solution for creating small-to medium-sized heterogeneous scenarios for evaluators to set up practice environments and competitions and to hone their skills. Second, the evaluatorcentric and extensible logger, ECEL, is a centralized management system that uses plugins for capturing and formatting evaluator data. We conclude the paper by providing a case study to demonstrate the setup and configuration of the platform along with a performance analysis.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122825227","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: 10

Performance of edge windowing for OFDM under non-linear power amplifier effects 非线性功放效应下OFDM边缘加窗性能研究

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170720

Çağrı Göken, Onur Dizdar

引用次数: 2

Indoor localization through trajectory tracking using neural networks 利用神经网络进行轨迹跟踪的室内定位

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170840

Mahi Abdelbar, R. Buehrer

{"title":"Indoor localization through trajectory tracking using neural networks","authors":"Mahi Abdelbar, R. Buehrer","doi":"10.1109/MILCOM.2017.8170840","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170840","url":null,"abstract":"Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor localization of mobile users is presented by exploiting trajectory tracking techniques using neural networks (NNs). The motion trajectories of indoor mobile users are tracked using conventional positioning algorithms, then a NN is applied to identify the current room location of a mobile user based on the tracked motion trajectory. Simulation results show that the trajectory-based NN is able to provide indoor location information at the room level with much higher accuracy in different scenarios, with an enhancement of up to 49% in correct room identification, as compared to positioning techniques based only on a single-point location estimate. In addition, miss-classification of the NN system will result in selecting one of the immediate neighboring rooms instead with at least 30% probability.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114423974","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: 3

Evasion and causative attacks with adversarial deep learning 对抗性深度学习的规避和因果攻击

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170807

Yi Shi, Y. Sagduyu

{"title":"Evasion and causative attacks with adversarial deep learning","authors":"Yi Shi, Y. Sagduyu","doi":"10.1109/MILCOM.2017.8170807","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170807","url":null,"abstract":"This paper presents a novel approach to launch and defend against the causative and evasion attacks on machine learning classifiers. As the preliminary step, the adversary starts with an exploratory attack based on deep learning (DL) and builds a functionally equivalent classifier by polling the online target classifier with input data and observing the returned labels. Using this inferred classifier, the adversary can select samples according to their DL scores and feed them to the original classifier. In an evasion attack, the adversary feeds the target classifier with test data after selecting samples with DL scores that are close to the decision boundary to increase the chance that these samples are misclassified. In a causative attack, the adversary feeds the target classifier with training data after changing the labels of samples with DL scores that are far away from the decision boundary to reduce the reliability of the training process. Results obtained for text and image classification show that the proposed evasion and causative attacks can significantly increase the error during test and training phases, respectively. A defense strategy is presented to change a small number of labels of the original classifier to prevent its reliable inference by the adversary and its effective use in evasion and causative attacks. These findings identify new vulnerabilities of machine learning and demonstrate that a proactive defense mechanism can reduce the impact of the underlying attacks.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"39 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933959","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: 36

Channel estimation for multi-way quantized distributed wireless relaying 多路量化分布式无线中继信道估计

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170735

Ahmad A. I. Ibrahim, Andrew C. Marcum, D. Love, J. Krogmeier

{"title":"Channel estimation for multi-way quantized distributed wireless relaying","authors":"Ahmad A. I. Ibrahim, Andrew C. Marcum, D. Love, J. Krogmeier","doi":"10.1109/MILCOM.2017.8170735","DOIUrl":"https://doi.org/10.1109/MILCOM.2017.8170735","url":null,"abstract":"A key focus of wireless communications research is on solutions that catalyze broadband access everywhere at anytime. Relaying has been introduced as a solution to enable communication between users who suffer from poor channel conditions. Distributed relay networks are a special case where spatial diversity is obtained by using a relay consisting of many geographically dispersed nodes. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit data simultaneously through the uplink to the relay nodes of the relay. Each node independently quantizes the observed signal to a few bits and broadcasts these bits through the band-limited downlink channel to the users. In this paper, we consider a multi-way quantized distributed relay network where the relay facilitates the communication between many users. For decoding purposes, we develop algorithms that can be employed by the users to estimate their uplink channels as well as the uplink channels observed by all other users when nodes perform simple sign quantization. A near maximum likelihood (nearML) channel estimator is derived. In addition, other sub-optimal estimators that are more computationally efficient than the nearML technique are also presented. Via simulation, we compare the performance of the proposed channel estimators.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122909728","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

Software Defined Networks (SDNs) of RF Internet of Things (RIOTs) on Unmanned Aerial Systems (UASs) 无人机系统(UASs)射频物联网(暴乱)的软件定义网络(sdn)

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170864

W. Watson, Steve Huntsman, J. Dolan

引用次数: 3

Spatial modulation based on reconfigurable antennas — A new air interface for the IoT 基于可重构天线的空间调制--物联网的新空中接口

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Pub Date : 2017-10-01 DOI: 10.1109/MILCOM.2017.8170856

M. Renzo

引用次数: 19