IEEE INFOCOM 2021 - IEEE Conference on Computer Communications最新文献_第2页

Popularity-Aware 360-Degree Video Streaming 流行度感知360度视频流

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488856

Xianda Chen, Tianxiang Tan, G. Cao

引用次数: 12

Invisible Poison: A Blackbox Clean Label Backdoor Attack to Deep Neural Networks 看不见的毒药:对深度神经网络的黑盒干净标签后门攻击

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488902

R. Ning, Jiang Li, Chunsheng Xin, Hongyi Wu

{"title":"Invisible Poison: A Blackbox Clean Label Backdoor Attack to Deep Neural Networks","authors":"R. Ning, Jiang Li, Chunsheng Xin, Hongyi Wu","doi":"10.1109/INFOCOM42981.2021.9488902","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488902","url":null,"abstract":"This paper reports a new clean-label data poisoning backdoor attack, named Invisible Poison, which stealthily and aggressively plants a backdoor in neural networks. It converts a regular trigger to a noised trigger that can be easily concealed inside images for training NN, with the objective to plant a backdoor that can be later activated by the trigger. Compared with existing data poisoning backdoor attacks, this newfound attack has the following distinct properties. First, it is a blackbox attack, requiring zero-knowledge of the target model. Second, this attack utilizes \"invisible poison\" to achieve stealthiness where the trigger is disguised as ‘noise’, and thus can easily evade human inspection. On the other hand, this noised trigger remains effective in the feature space to poison training data. Third, the attack is practical and aggressive. A backdoor can be effectively planted with a small amount of poisoned data and is robust to most data augmentation methods during training. The attack is fully tested on multiple benchmark datasets including MNIST, Cifar10, and ImageNet10, as well as application specific data sets such as Yahoo Adblocker and GTSRB. Two countermeasures, namely Supervised and Unsupervised Poison Sample Detection, are introduced to defend the attack.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130925526","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: 17

Turbocharging Deep Backscatter Through Constructive Power Surges with a Single RF Source 涡轮增压深后向散射通过建设性功率浪涌与单一射频源

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488871

Zhenlin An, Qiongzheng Lin, Qingrui Pan, Lei Yang

{"title":"Turbocharging Deep Backscatter Through Constructive Power Surges with a Single RF Source","authors":"Zhenlin An, Qiongzheng Lin, Qingrui Pan, Lei Yang","doi":"10.1109/INFOCOM42981.2021.9488871","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488871","url":null,"abstract":"Backscatter networks are becoming a promising solution for embedded sensing. In these networks, backscatter sensors are deeply implanted inside objects or living beings and form a deep backscatter network (DBN). The fundamental challenges in DBNs are the significant attenuation of the wireless signal caused by environmental materials (e.g., water and bodily tissues) and the miniature antennas of the implantable backscatter sensors, which prevent existing backscatter networks from powering sensors beyond superficial depths. This study presents RiCharge, a turbocharging solution that enables powering up and communicating with DBNs through a single augmented RF source, which allows existing backscatter sensors to serve DBNs at zero startup cost. The key contribution of RiCharge is the turbocharging algorithm that utilizes RF surges to induce constructive power surges at deep backscatter sensors in accordance with the FCC regulations, for overcoming the turn-on voltage barrier. RiCharge is implemented in commodity devices, and the evaluation result reveals that RiCharge can use only a single RF source to power up backscatter sensors at 60 m distance in the air (i.e., 10x longer than a commercial off-the-shelf reader) and 50 cm-depth under water (i.e., 2x deeper than the previous record).","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132939876","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

Bringing Fairness to Actor-Critic Reinforcement Learning for Network Utility Optimization 为网络效用优化引入行为-评价强化学习的公平性

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488823

Jingdi Chen, Yimeng Wang, T. Lan

{"title":"Bringing Fairness to Actor-Critic Reinforcement Learning for Network Utility Optimization","authors":"Jingdi Chen, Yimeng Wang, T. Lan","doi":"10.1109/INFOCOM42981.2021.9488823","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488823","url":null,"abstract":"Fairness is a crucial design objective in virtually all network optimization problems, where limited system resources are shared by multiple agents. Recently, reinforcement learning has been successfully applied to autonomous online decision making in many network design and optimization problems. However, most of them try to maximize the long-term (discounted) reward of all agents, without taking fairness into account. In this paper, we propose a family of algorithms that bring fairness to actorcritic reinforcement learning for optimizing general fairness utility functions. In particular, we present a novel method for adjusting the rewards in standard reinforcement learning by a multiplicative weight depending on both the shape of fairness utility and some statistics of past rewards. It is shown that for proper choice of the adjusted rewards, a policy gradient update converges to at least a stationary point of general αfairness utility optimization. It inspires the design of fairness optimization algorithms in actor-critic reinforcement learning. Evaluations show that the proposed algorithm can be easily deployed in real-world network optimization problems, such as wireless scheduling and video QoE optimization, and can significantly improve the fairness utility value over previous heuristics and learning algorithms.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133050387","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: 16

Your Home is Insecure: Practical Attacks on Wireless Home Alarm Systems 你的家不安全:对无线家庭报警系统的实际攻击

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488873

Tao Li, Dianqi Han, Jiawei Li, Ang Li, Yan Zhang, Rui Zhang, Yanchao Zhang

引用次数: 2

Bound Inference and Reinforcement Learning-based Path Construction in Bandwidth Tomography 带宽层析成像中基于边界推理和强化学习的路径构建

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488691

Cuiying Feng, Jian An, Kui Wu, Jianping Wang

{"title":"Bound Inference and Reinforcement Learning-based Path Construction in Bandwidth Tomography","authors":"Cuiying Feng, Jian An, Kui Wu, Jianping Wang","doi":"10.1109/INFOCOM42981.2021.9488691","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488691","url":null,"abstract":"Inferring the bandwidth of internal links from the bandwidth of end-to-end paths, so-termed bandwidth tomography, is a long-standing open problem in the network tomography literature. The difficulty is due to the fact that no existing mathematical tool is directly applicable to solve the inverse problem with a set of min-equations. We systematically tackle this challenge by designing a polynomial-time algorithm that returns the exact bandwidth value for all identifiable links and the tightest error bound for unidentifiable links for a given set of measurement paths. When measurement paths are not given in advance, we prove the hardness of building measurement paths that can be used for deriving the global tightest error bounds for unidentifiable links. Accordingly, we develop a reinforcement learning (RL) approach for measurement path construction, that utilizes the special knowledge in bandwidth tomography and integrates both offline training and online prediction. Evaluation results with real-world ISP as well as simulated networks demonstrate that compared to other path construction methods, Random and Diversity Preferred, our RL-based path construction method can build measurement paths that result in much smaller average error bound of link bandwidth.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124574506","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

DRL-OR: Deep Reinforcement Learning-based Online Routing for Multi-type Service Requirements DRL-OR:基于深度强化学习的多类型业务需求在线路由

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488736

Chenyi Liu, Mingwei Xu, Yuan Yang, Nan Geng

{"title":"DRL-OR: Deep Reinforcement Learning-based Online Routing for Multi-type Service Requirements","authors":"Chenyi Liu, Mingwei Xu, Yuan Yang, Nan Geng","doi":"10.1109/INFOCOM42981.2021.9488736","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488736","url":null,"abstract":"Emerging applications raise critical QoS requirements for the Internet. The improvements of flow classification technologies, software defined networks (SDN), and programmable network devices make it possible to fast identify users’ requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR, an online routing algorithm using multi-agent deep reinforcement learning. DRL-OR organizes the agents to generate routes in a hop-by-hop manner, which inherently has good scalability. It adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn an appropriate routing policy for different types of flow requirements. To guarantee the reliability and accelerate the online learning process, we further introduce safe learning mechanism to DRL-OR. We implement DRL-OR under SDN architecture and conduct Mininet-based experiments by using real network topologies and traffic traces. The results validate that DRL-OR can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, and partial deployment.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012936","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: 16

Live Gradient Compensation for Evading Stragglers in Distributed Learning 分布式学习中离散体回避的动态梯度补偿

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488815

Jian Xu, Shao-Lun Huang, Linqi Song, Tian Lan

{"title":"Live Gradient Compensation for Evading Stragglers in Distributed Learning","authors":"Jian Xu, Shao-Lun Huang, Linqi Song, Tian Lan","doi":"10.1109/INFOCOM42981.2021.9488815","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488815","url":null,"abstract":"The training efficiency of distributed learning systems is vulnerable to stragglers, namely, those slow worker nodes. A naive strategy is performing the distributed learning by incor-porating the fastest K workers and ignoring these stragglers, which may induce high deviation for non-IID data. To tackle this, we develop a Live Gradient Compensation (LGC) strategy to incorporate the one-step delayed gradients from stragglers, aiming to accelerate learning process and utilize the stragglers simultaneously. In LGC framework, mini-batch data are divided into smaller blocks and processed separately, which makes the gradient computed based on partial work accessible. In addition, we provide theoretical convergence analysis of our algorithm for non-convex optimization problem under non-IID training data to show that LGC-SGD has almost the same convergence error as full synchronous SGD. The theoretical results also allow us to quantify a novel tradeoff in minimizing training time and error by selecting the optimal straggler threshold. Finally, extensive simulation experiments of image classification on CIFAR-10 dataset are conducted, and the numerical results demonstrate the effectiveness of our proposed strategy.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115013546","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: 8

WebMythBusters: An In-depth Study of Mobile Web Experience WebMythBusters:对移动网络体验的深入研究

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488671

Seonghoon Park, Yonghun Choi, H. Cha

{"title":"WebMythBusters: An In-depth Study of Mobile Web Experience","authors":"Seonghoon Park, Yonghun Choi, H. Cha","doi":"10.1109/INFOCOM42981.2021.9488671","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488671","url":null,"abstract":"The quality of experience (QoE) is an important issue for users when accessing the web. Although many metrics have been designed to estimate the QoE in the desktop environment, few studies have confirmed whether the QoE metrics are valid in the mobile environment. In this paper, we ask questions regarding the validity of using desktop-based QoE metrics for the mobile web and find answers. We first classify the existing QoE metrics into several groups according to three criteria and then identify the differences between the mobile and desktop environments. Based on the analysis, we ask three research questions and develop a system, called WebMythBusters, for collecting and analyzing mobile web experiences. Through an extensive analysis of the collected user data, we find that (1) the metrics focusing on fast completion or fast initiation of the page loading process cannot estimate the actual QoE, (2) the conventional scheme of calculating visual progress is not appropriate, and (3) focusing only on the above-the-fold area is not sufficient in the mobile environment. The findings indicate that QoE metrics designed for the desktop environment are not necessarily adequate for the mobile environment, and appropriate metrics should be devised to reflect the mobile web experience.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551429","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

Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets 分布式数据集上高效保护隐私的离群点检测

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488710

Guanghong Lu, Chunhui Duan, Guohao Zhou, Xuan Ding, Yunhao Liu

{"title":"Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets","authors":"Guanghong Lu, Chunhui Duan, Guohao Zhou, Xuan Ding, Yunhao Liu","doi":"10.1109/INFOCOM42981.2021.9488710","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488710","url":null,"abstract":"The ability to detect outliers is crucial in data mining, with widespread usage in many fields, including fraud detection, malicious behavior monitoring, health diagnosis, etc. With the tremendous volume of data becoming more distributed than ever, global outlier detection for a group of distributed datasets is particularly desirable. In this work, we propose PIF (Privacy-preserving Isolation Forest), which can detect outliers for multiple distributed data providers with high efficiency and accuracy while giving certain security guarantees. To achieve the goal, PIF makes an innovative improvement to the traditional iForest algorithm, enabling it in distributed environments. With a series of carefully-designed algorithms, each participating party collaborates to build an ensemble of isolation trees efficiently without disclosing sensitive information of data. Besides, to deal with complicated real-world scenarios where different kinds of partitioned data are involved, we propose a comprehensive schema that can work for both horizontally and vertically partitioned data models. We have implemented our method and evaluated it with extensive experiments. It is demonstrated that PIF can achieve comparable AUC to existing iForest on average and maintains a linear time complexity without privacy violation.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114900806","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