2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)最新文献_第3页

A Robust Anonymous Authentication Scheme using Biometrics for Digital Rights Management System 数字版权管理系统中基于生物特征的鲁棒匿名认证方案

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583219

Muhammad A. Khan, Anwer Ghani, M. Obaidat, P. Vijayakumar, Khwaja Mansoor, Shehzad Ashraf Chaudhry

{"title":"A Robust Anonymous Authentication Scheme using Biometrics for Digital Rights Management System","authors":"Muhammad A. Khan, Anwer Ghani, M. Obaidat, P. Vijayakumar, Khwaja Mansoor, Shehzad Ashraf Chaudhry","doi":"10.1109/CCCI52664.2021.9583219","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583219","url":null,"abstract":"As digital content transmission through the internet is convenient and quick, so the outspread of digital content is very high. However, along with this incredible speed and ease, current communication technologies and computers have also brought with them plenty of digital rights management complications. Digital Rights Management Systems are designed to limit the access to the utilization, alternation, and distribution of persevered digital content. This article scrutinized two recent schemes of Lee et al. and Yu et al. and it is found that these schemes are suspected to an insider attack, stolen smart-card attack, Daniel of services (Dos) attack, and impersonation attack. Furthermore, their proposal also suffers from incorrect issues. To fix these flaws, a robust anonymous authentication scheme using biometrics for Digital Rights Management System is proposed in this article. The proposed scheme is checked for correctness and its security is proved through BAN logic. The performance of the scheme is also analyzed using computation time and communication time. The results show that the designed scheme is highly secure with the same computation and communication cost as the existing protocols.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127902870","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: 7

XAI-AV: Explainable Artificial Intelligence for Trust Management in Autonomous Vehicles XAI-AV:可解释的自动驾驶汽车信任管理人工智能

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583190

Harsh Mankodiya, M. Obaidat, Rajesh Gupta, S. Tanwar

{"title":"XAI-AV: Explainable Artificial Intelligence for Trust Management in Autonomous Vehicles","authors":"Harsh Mankodiya, M. Obaidat, Rajesh Gupta, S. Tanwar","doi":"10.1109/CCCI52664.2021.9583190","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583190","url":null,"abstract":"Artificial intelligence (AI) is the most looked up technology with a diverse range of applications across all the fields, whether it is intelligent transportation systems (ITS), medicine, healthcare, military operations, or others. One such application is autonomous vehicles (AVs), which comes under the category of AI in ITS. Vehicular Adhoc Networks (VANET) makes communication possible between AVs in the system. The performance of each vehicle depends upon the information exchanged between AVs. False or malicious information can perturb the whole system leading to severe consequences. Hence, the detection of malicious vehicles is of utmost importance. We use machine learning (ML) algorithms to predict the flaw in the data transmitted. Recent papers that used the stacking ML approach gave an accuracy of 98.44%. Decision tree-based random forest is used to solve the problem in this paper. We achieved accuracy and F1 score of 98.43% and 98.5% respectively on the VeRiMi dataset in this paper. Explainable AI (XAI) is the method and technique to make the complex black-box ML and deep learning (DL) models more interpretable and understandable. We use a particular model interface of the evaluation metrics to explain and measure the model’s performance. Applying XAI to these complex AI models can ensure a cautious use of AI for AVs.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125674786","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: 12

DPSGD Strategies for Cross-Silo Federated Learning 跨筒仓联邦学习的DPSGD策略

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583220

Matthieu Moreau, Tarek Benkhelif

引用次数: 0

Capsule: All you need to know about Tactile Internet in a Nutshell 胶囊:所有你需要知道的触觉互联网在一个果壳

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583213

Rajesh Gupta, S. Tanwar, M. Obaidat, Sudhanshu Tyagi, Neeraj Kumar

{"title":"Capsule: All you need to know about Tactile Internet in a Nutshell","authors":"Rajesh Gupta, S. Tanwar, M. Obaidat, Sudhanshu Tyagi, Neeraj Kumar","doi":"10.1109/CCCI52664.2021.9583213","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583213","url":null,"abstract":"Machine-to-machine and device-to-device communications are very common in the era of the Internet of things (IoT). In 5G-enabled IoT, massive machine type communication and enhanced Mobile broadband are two supporting protocols ensuring communication among a large number of devices. On the other side, for human-to-machine communication, end-to-end latency, resource availability, high reliability, and end-to-end security are the major hurdles for several applications deployed in different network domains. The above-mentioned issues require a unique solution to maintain Quality of Service and Quality of Experience for industry 4.0 based applications. Therefore, to mitigate the aforementioned issues, here, we present the Tactile Internet, a new technology with its various components in a nutshell. Existing proposals reveal that Tactile Internet can form the interactions between virtual objects and the real environment by maintaining 1ms latency, 99.999% availability, and end-to-end security. Hence, it works like middleware to meet out the ultra-reliable low latency requirements of future 5G-based applications. Moreover, the article provides the possible use cases for Tactile Internet and its usage. Finally, we evaluate the results of applications with Tactile Internet and LTE-A communication networks in the context of data access latency and connection density. Applications with Tactile Internet as a backbone network outperform the traditional LTE-A networks.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115267054","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: 2

Towards Safer Roads: An Efficient VANET-based Pedestrian Protection Scheme 迈向更安全的道路:有效的行人保护计划

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583221

Khaled Rabieh, A. Aydogan, Marianne A. Azer

{"title":"Towards Safer Roads: An Efficient VANET-based Pedestrian Protection Scheme","authors":"Khaled Rabieh, A. Aydogan, Marianne A. Azer","doi":"10.1109/CCCI52664.2021.9583221","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583221","url":null,"abstract":"About 1.2 million people lose their lives on roads yearly due to accidents despite the emerging and uprising technology in contemporary vehicles. In addition, 4.4 million people were seriously injured and required medical attention in crashes last year. By employing Vehicle-to-Pedestrian (V2P) communication between drivers and vulnerable road users, fewer casualties are likely to occur and roads are expected to be much safer. In this paper, we propose a lightweight scheme to protect vulnerable road users based on communication between smartphones and on-board units installed in vehicles. Initially, the signal strength is used to estimate the distance between vehicles and pedestrians and predict the occurrence of a collision. Since signal strength alone can result in false alarms, we propose a collision detection algorithm to confirm a collision. The algorithm is run on both sides; the drivers and vulnerable road users to give appropriate and real-time warnings of a potential accident/collision. Vehicles and road users exchange their Global Positioning System (GPS) locations using Dedicated Short Range Communications (DSRC). The algorithm constructs a vector representing the vehicle path and uses efficient and simple mathematical operations to determine if there is a possibility of collision or not. Our scheme contributes to the safety applications of vehicular ad hoc networks. Our experiment’s results confirm that the proposed scheme can effectively detect collisions with minimum computation overhead.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133520265","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: 2

Catching Unusual Traffic Behavior using TF–IDF-based Port Access Statistics Analysis 基于tf - idf的端口访问统计分析捕获异常流量行为

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583212

K. Shima

引用次数: 1

Reference-based Image Super-Resolution by Dual-Variational AutoEncoder 基于参考的图像超分辨率双变分自编码器

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583193

Mengyao Yang, Junpeng Qi

引用次数: 1

Classification of Soil Images using Convolution Neural Networks 基于卷积神经网络的土壤图像分类

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583192

Kajal Chatterjee, M. Obaidat, Debabrata Samanta, B. Sadoun, SK Hafizul Islam, Rajdeep Chatterjee

{"title":"Classification of Soil Images using Convolution Neural Networks","authors":"Kajal Chatterjee, M. Obaidat, Debabrata Samanta, B. Sadoun, SK Hafizul Islam, Rajdeep Chatterjee","doi":"10.1109/CCCI52664.2021.9583192","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583192","url":null,"abstract":"Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133813744","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

Discover Community Structure in Network by Optimization Algorithm Based on Modular Function 基于模块化函数的优化算法发现网络中的社区结构

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583200

Xiaoling Guo, Y. Yang, Xinyu Song, Hongmiao Yao, Fudong Zhang

引用次数: 0

A Traffic Prediction assisted Routing Algorithm for Elastic Optical Networks 弹性光网络流量预测辅助路由算法

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583188

A. Valkanis, Georgios I. Papadimitriou, Petros Nicopolitidis, G. Beletsioti, Emmanouel Varvarigos

引用次数: 8