2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献_第3页

Security Analysis of Docker Containers for ARM Architecture 基于ARM架构的Docker容器安全性分析

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00025

Md. Sadun Haq, A. Tosun, T. Korkmaz

{"title":"Security Analysis of Docker Containers for ARM Architecture","authors":"Md. Sadun Haq, A. Tosun, T. Korkmaz","doi":"10.1109/SEC54971.2022.00025","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00025","url":null,"abstract":"Containers provide a better performance, faster deployment than virtual machines and provide near-native performance, with isolation and security drawbacks. Although the security of containers for the Intel architecture has been investigated in more detail, there is limited work on the security of containers for the ARM architecture. In this paper, we address this gap in research and focus on the security of containers designed for the ARM architecture, which is heavily used in IoT devices. Edge computing offers many advantages, including reduced latency and resource requirements at the cloud because data can be processed at the edge before it is sent to the cloud. Using containers at the edge nodes of IoT-Edge-Cloud systems can enhance such advantages at the cost of increasing security vulnerabilities in such systems. Therefore, it is essential to investigate the security of containers designed for the ARM architecture. Accordingly, we obtained official ARM images from DockerHub and used various security tools to scan these ARM images. We found that 72% of all the vulnerabilities show varying severity levels and each tool seems to work best for particular base images. We investigated how each tool detects sub-packages and achieves a different hit ratio while none of them alone can detect at least 80% of all the vulnerabilities. In addition, we also investigated how the Docker images and their vulnerability landscape change over a period of six months by running the scanning tools twice. Finally, we also conducted a dynamic analysis of some of the images on the Raspberry Pi and study their effects. We believe this paper will facilitate the use of ARM containers at the ARM-based edge nodes by addressing security issues.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351938","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

Poster: Imitation Learning for Hearing Loss Detection with Cortical Speech-Evoked Responses 海报:用皮质言语诱发反应进行听力损失检测的模仿学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00045

Cicelia Siu, Beiyu Lin

引用次数: 0

Poster: Autonomous mobile robot for indoor enhanced living 海报:室内增强生活的自主移动机器人

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00038

Prabhjot Kaur, Weisong Shi

引用次数: 0

Rev: A Video Engine for Object Re-identification at the City Scale Rev:城市尺度下物体再识别的视频引擎

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00022

Tiantu Xu, Kaiwen Shen, Yang Fu, Humphrey Shi, F. Lin

引用次数: 0

Privacy-Preserving Public Auditing for Shared Data in Mobile Cloud Storage 移动云存储中共享数据的隐私保护公共审计

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00074

Xia'nan Zhao, Dongsheng Wang

引用次数: 0

Scaling Vehicle Routing Problem Solvers with QUBO-based Specialized Hardware 基于qubo的专用硬件的车辆路径问题解决方案

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00056

Hanjing Xu, Hayato Ushijima-Mwesigwa, Indradeep Ghosh

{"title":"Scaling Vehicle Routing Problem Solvers with QUBO-based Specialized Hardware","authors":"Hanjing Xu, Hayato Ushijima-Mwesigwa, Indradeep Ghosh","doi":"10.1109/SEC54971.2022.00056","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00056","url":null,"abstract":"The Vehicle Routing Problem (VRP) is an NP-Hard optimization problem that has been widely studied over the last decades due to its wide practical applications. The goal of the VRP is to design efficient routes for vehicles performing service functions such as the distribution of goods from one or more central locations. There exist many variants to the VRP. These variations arise due to application-specific constraints such as vehicle capacities, delivery time windows, split deliveries, and many more. Due to the complexity of the problem, solvers are usually designed for specific variants and do not generally scale well to larger problem instances. As we approach the physical scaling limits of Moore's law, different novel special-purpose hardware are being designed for solving combinatorial optimization problems such as quantum and quantum-inspired devices. However, these devices are able to solve problems up to fixed variable sizes. In this work, we carry out experiments on the Fujitsu Digital Annealer (DA), quantum-inspired hardware, and show that we are able to get near-optimal results for instances of the Capacitated VRP (CVRP) that can be directly solved on the DA. We further develop a hybrid method for solving large-scale VRP instances. In order to achieve this, we develop a novel candidate route generation method, whose core solves a multi-objective clustering problem on the DA. Once a set of candidate routes has been selected, we then solve a Set Partitioning Problem on the DA to get a final solution. We apply our method to the CVRP and the CVRP with Time-Windows and show that we are able to get near-optimal solutions for problem instances significantly larger the base solvers could solve directly, within the same amount of computation time.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114194197","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 quantum algorithm for syndrome decoding of classical error-correcting linear block codes 经典纠错线性分组码证型译码的量子算法

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00069

Mainak Bhattacharyya, Ankur Raina

引用次数: 2

Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression 海报:使边缘辅助激光雷达感知对有损点云压缩具有鲁棒性

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00036

Jin Heo, Gregoire Phillips, Per-Erik Brodin, Ada Gavrilovska

引用次数: 0

Classification With Unimodular Matrices In Hybrid Models 混合模型中单模矩阵的分类

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00063

Dominic Pasquali

引用次数: 0

Poster: Unsupervised Learning for Extreme Space Weather Detection based on Spectrograms 海报:基于谱图的极端空间天气检测的无监督学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00046

L. Barbier, Beiyu Lin

{"title":"Poster: Unsupervised Learning for Extreme Space Weather Detection based on Spectrograms","authors":"L. Barbier, Beiyu Lin","doi":"10.1109/SEC54971.2022.00046","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00046","url":null,"abstract":"Approximately 2,000 satellites orbiting Earth relay telecommunications, broadcasting, and data communications to and from different locations globally. For example, in 2018,8.4 million households re-lied on satellite internet in the United States. However, extreme space weather, space environment phenomena driven by plasma be-tween stars and planets, can harm satellites and the global data and internet communications, and affect near-Earth space. Extracting features that represents those plasma waves requires highly-trained space scientists. We want to design machine learning (ML) meth-ods to automatically design features and extract information from plasma waves for the early detection of extreme space weather. To do that and to leverage the rich and state-of-the-art algorithms in computer vision, we first use Heliophysics Audified: Resonances in Plasmas (HARP) Sonification Data Processing package [1] to con-vert magnetospheric Ultra-Low Frequency (ULF) waves to sound and spectrograms. We then utilize unsupervised learning meth-ods to cluster plasma waves into different groups to capture the commonalities and differences between those activities. This initial and pilot exploration in the field offers the potential of practical applications of ML to space science field. The results will help with satellite-based internet and communications as part of the edge computing community.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708535","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