2020 29th International Conference on Computer Communications and Networks (ICCCN)最新文献_第6页

Privacy-Preserving Selective Video Surveillance 保护隐私的选择性视频监控

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209688

Alem Fitwi, Yu Chen

{"title":"Privacy-Preserving Selective Video Surveillance","authors":"Alem Fitwi, Yu Chen","doi":"10.1109/ICCCN49398.2020.9209688","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209688","url":null,"abstract":"The pervasive and intrusive surveillance practices have raised a widespread concern amongst zillions of people about the invasion of their privacy. The privacy breaches in the existing mass-surveillance system are mainly attributed to the exploits of vulnerabilities by adversaries and abuse of cameras by people in charge of them. As a result, there has been a tremendously pressing demand from the public to make the surveillance system privacy-conscious. In this paper, we propose PriSev, a privacy-preserving selective video surveillance method, which enables selective-surveillance where only video frames containing aggressive and suspicious behavioral patterns, like gun brandishing or/and fist-raising, are made available for view by security personnel in the surveillance operation center and for storage. By introducing a lightweight dynamic chaotic image enciphering (DyCIE) scheme, the proposed PriSev method enables onsite object detection and frame encryption at the network edge where the video is created. At the fog/cloud layer, frame decryption is efficiently performed followed by deep-neural-network (DNN) based frame-filtering and selective storage that runs on a surveillance server. In addition, a multiagent system is introduced for the exchange of deciphering keys between the sending and receiving agents. Extensive experimental study and performance analyses corroborate that the proposed PriSev method is able to efficiently perform a privacy-preserving selective surveillance in real-time.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114319958","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

Joint Energy Optimization of Cooling Systems and Virtual Machine Consolidation in Data Centers 数据中心冷却系统联合能量优化与虚拟机整合

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209712

Hai Liu, W. Wong, Shujin Ye, Y. Chris

{"title":"Joint Energy Optimization of Cooling Systems and Virtual Machine Consolidation in Data Centers","authors":"Hai Liu, W. Wong, Shujin Ye, Y. Chris","doi":"10.1109/ICCCN49398.2020.9209712","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209712","url":null,"abstract":"Minimizing energy consumption of data centers is important to reduce carbon emissions. Virtual machines (VMs) consolidation is a typical technique to utilize the available data center resources and thus improve energy efficiency. The cooling systems consume up to 50% of the total data center electricity. In this work, we investigate the joint energy optimization of cooling systems and VM consolidations in cloud data centers. We propose a cooling-aware VM consolidation (CAVC for short) algorithm to the problem. The CAVC algorithm is a two-stage solution: 1) we first relax the constraints of the problem and determine an optimal number of physical machines (PMs) and an optimal CPU utilization of the PMs that yields the minimum cooling power; and 2) based on the initial solution of the first stage, we consolidate the VMs into the predetermined PMs with the predetermined CPU utilization ratio as much as possible. To the best of authors’ knowledge, this is the first work that jointly considers the VM consolidation and the cooling systems in minimizing energy consumption of cloud data centers. We derive an approximation ratio of CAVC over the optimal solution. The real-world data set (i.e., Google cluster data) is adopted in the simulations and the results show that the CAVC algorithm yields very close energy consumption to the theoretical lower bound.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116313270","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

MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning MLGuard:在保护隐私的分布式协作学习中减轻中毒攻击

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209670

Youssef Khazbak, Tianxiang Tan, G. Cao

{"title":"MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning","authors":"Youssef Khazbak, Tianxiang Tan, G. Cao","doi":"10.1109/ICCCN49398.2020.9209670","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209670","url":null,"abstract":"Distributed collaborative learning has enabled building machine learning models from distributed mobile users’ data. It allows the server and users to collaboratively train a learning model where users only share model parameters with the server. To protect privacy, the server can use secure multiparty computation to learn the global model without revealing users’ parameter updates in the clear. However this privacy preserving distributed learning opens the door to poisoning attacks, where malicious users poison their training data to maliciously influence the behavior of the global model. In this paper, we propose MLGuard, a privacy preserving distributed collaborative learning system with poisoning attack mitigation. MLGuard employs lightweight secret sharing scheme and a novel poisoning attack mitigation technique. We address several challenges such as preserving users’ privacy, mitigating poisoning attacks, respecting resource constraints of mobile devices, and scaling to large number of users. Evaluation results demonstrate the effectiveness of MLGuard on building high accurate learning models with the existence of malicious users, while imposing minimal communication cost on mobile devices.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"30 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128230298","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: 29

A Learning-based Data Augmentation for Network Anomaly Detection 基于学习的网络异常检测数据增强

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209598

Mohammad Al Olaimat, Dongeun Lee, Youngsoo Kim, Jong-Hoi Kim, Jinoh Kim

{"title":"A Learning-based Data Augmentation for Network Anomaly Detection","authors":"Mohammad Al Olaimat, Dongeun Lee, Youngsoo Kim, Jong-Hoi Kim, Jinoh Kim","doi":"10.1109/ICCCN49398.2020.9209598","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209598","url":null,"abstract":"While machine learning technologies have been remarkably advanced over the past several years, one of the fundamental requirements for the success of learning-based approaches would be the availability of high-quality data that thoroughly represent individual classes in a problem space. Unfortunately, it is not uncommon to observe a significant degree of class imbalance with only a few instances for minority classes in many datasets, including network traffic traces highly skewed toward a large number of normal connections while very small in quantity for attack instances. A well-known approach to addressing the class imbalance problem is data augmentation that generates synthetic instances belonging to minority classes. However, traditional statistical techniques may be limited since the extended data through statistical sampling should have the same density as original data instances with a minor degree of variation. This paper takes a learning-based approach to data augmentation to enable effective network anomaly detection. One of the critical challenges for the learning-based approach is the mode collapse problem resulting in a limited diversity of samples, which was also observed from our preliminary experimental result. To this end, we present a novel \"Divide-Augment-Combine\" (DAC) strategy, which groups the instances based on their characteristics and augments data on a group basis to represent a subset independently using a generative adversarial model. Our experimental results conducted with two recently collected public network datasets (UNSW-NB15 and IDS-2017) show that the proposed technique enhances performances up to 21.5% for identifying network anomalies.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133353080","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: 5

Residual Neural Networks for Heterogeneous Smart Device Localization in IoT Networks 物联网网络中异构智能设备定位的残差神经网络

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209703

Pandey Pandey, Piyush Tiwary, Sudhir Kumar, Sajal K. Das

引用次数: 5

ZIDX: A Generic Framework for Random Access to BGP Records in Compressed MRT Datasets 压缩MRT数据集中随机访问BGP记录的通用框架

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209595

Omer F. Ozarslan, K. Saraç

引用次数: 1

Efficient Multicast Labelling for OpenFlow-Based Switches 基于openflow交换机的高效组播标记

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209604

Ci-Hung Ciou, Pi-Chung Wang

引用次数: 0

TA-MAC: A Traffic-Aware TDMA MAC Protocol for Safety Message Dissemination in MEC-assisted VANETs 一种基于流量感知的TDMA MAC协议，用于mec辅助vanet中的安全消息传播

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209706

Dongxiao Deng, Wenbi Rao, Bingyi Liu, D. Jia, Yong Sheng, Jianping Wang, Shengwu Xiong

{"title":"TA-MAC: A Traffic-Aware TDMA MAC Protocol for Safety Message Dissemination in MEC-assisted VANETs","authors":"Dongxiao Deng, Wenbi Rao, Bingyi Liu, D. Jia, Yong Sheng, Jianping Wang, Shengwu Xiong","doi":"10.1109/ICCCN49398.2020.9209706","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209706","url":null,"abstract":"Vehicular ad hoc networks (VANETs) have been widely recognized as a promising solution to improve traffic safety and efficiency for the ability to provide situation awareness even though the potential dangers and traffic anomalies are out of the visual range. In VANETs, time-division multiple access (TDMA) based overlay protocols can prevent transmission collisions, and play an important role in providing an efficient communication channel. However, due to high vehicle mobility and time-varying traffic flow, the existing TDMA-based slot allocation approaches cannot fully utilize the channel resources, which may result in high transmission delay and packet collision. To overcome these shortcomings, we propose a traffic-aware TDMA-based MAC (TA-MAC) protocol which utilizes the capability of mobile edge computing (MEC) in this paper. Specifically, based on MEC and vehicle-to-road-side-units (V2R) communications, a traffic-aware mechanism is first proposed to estimate the traffic condition on the road segment. Then, we propose a new slot assignment method that aims at guaranteeing the high channel utilization and low delay of safety message under dynamic traffic conditions. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed protocol.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125560823","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

Enabling Surveillance Cameras to Navigate 启用监控摄像头导航

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209695

Liang Dong, Jingao Xu, Guoxuan Chi, Danyang Li, Xinglin Zhang, Jianbo Li, Q. Ma, Zheng Yang

{"title":"Enabling Surveillance Cameras to Navigate","authors":"Liang Dong, Jingao Xu, Guoxuan Chi, Danyang Li, Xinglin Zhang, Jianbo Li, Q. Ma, Zheng Yang","doi":"10.1109/ICCCN49398.2020.9209695","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209695","url":null,"abstract":"Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians, however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this paper, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smart-phones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68m, outperforming the state-of-the-art systems by > 75%.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869370","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

Optimizing Scan Times of BLE Scanning Systems 优化BLE扫描系统的扫描时间

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209607

J. Daigle, George Humphrey, Henry C. Lena, Avijit Sarker

引用次数: 1