2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)最新文献_第5页

A Password Strength Evaluation Algorithm based on Sensitive Personal Information 一种基于敏感个人信息的密码强度评估算法

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00211

Xinchun Cui, Xueqing Li, Yiming Qin, Yong Ding

引用次数: 3

Trusted Network Slicing among Multiple Mobile Network Operators 多移动网络运营商之间的可信网络切片

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00150

M. Yousuf, Mingjun Wang, Zheng Yan, Fawad Khan

{"title":"Trusted Network Slicing among Multiple Mobile Network Operators","authors":"M. Yousuf, Mingjun Wang, Zheng Yan, Fawad Khan","doi":"10.1109/TrustCom50675.2020.00150","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00150","url":null,"abstract":"5G mobile networks are expected to be much bigger in size, faster in speeds and better in scalability, providing varied services to different users and businesses in contrast to previous networks. 5G will also help enabling new business models and use cases. “Network Slicing” is a driving architectural concept for multi-tenancy. Network Slicing enables Mobile Network Operators (MNOs) to deploy different services over shared physical infrastructure, increasing inter-operator resource sharing. As 5G is still in its nascent, inter operator cooperation is an area that requires immediate attention of research. Traditional inter operator trust relationship models cannot fully comprehend the needs of 5G networks. In this paper, we propose an Intel SGX based multi-MNO cooperation scheme for trusted, dynamic and efficient network slice sharing in order to support inter-operator trustworthy collaboration. Furthermore, we developed a Proof of Concept of our proposed scheme using Intel SGX, flask framework and Docker containers. The obtained results indicate the applicability of the proposed scheme with little effect on performance.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132555719","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

Detection of Hate Tweets using Machine Learning and Deep Learning 使用机器学习和深度学习检测仇恨推文

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00103

Lida Ketsbaia, B. Issac, Xiaomin Chen

{"title":"Detection of Hate Tweets using Machine Learning and Deep Learning","authors":"Lida Ketsbaia, B. Issac, Xiaomin Chen","doi":"10.1109/TrustCom50675.2020.00103","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00103","url":null,"abstract":"Cyberbullying has become a highly problematic occurrence due to its potential of anonymity and its ease for others to join in the harassment of victims. The distancing effect that technological devices have, has led to cyberbullies say and do harsher things compared to what is typical in a traditional face-to-face bullying situation. Given the great importance of the problem, detection is becoming a key area of cyberbullying research. Therefore, it is highly necessary for a framework to accurately detect new cyberbullying instances automatically. To review the machine learning and deep learning approaches, two datasets were used. The first dataset was provided by the University of Maryland consisting of over 30,000 tweets, whereas the second dataset was based on the article ‘Automated Hate Speech Detection and the Problem of Offensive Language’ by Davidson et al., containing roughly 25,000 tweets. The paper explores machine learning approaches using word embeddings such as DBOW (Distributed Bag of Words) and DMM (Distributed Memory Mean) and the performance of Word2vec Convolutional Neural Networks (CNNs) to classify online hate.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797461","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: 14

Welcome Messages from IEEE TrustCom 2020 Program Chairs IEEE TrustCom 2020项目主席欢迎辞

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/trustcom50675.2020.00006

引用次数: 0

Traffic Classification of User Behaviors in Tor, I2P, ZeroNet, Freenet Tor、I2P、ZeroNet、Freenet中用户行为的流量分类

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00064

Yuzong Hu, Futai Zou, Linsen Li, P. Yi

{"title":"Traffic Classification of User Behaviors in Tor, I2P, ZeroNet, Freenet","authors":"Yuzong Hu, Futai Zou, Linsen Li, P. Yi","doi":"10.1109/TrustCom50675.2020.00064","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00064","url":null,"abstract":"In recent years, more and more anonymous network have been developed. Since user's identity is difficult to trace in anonymous networks, many illegal activities are carried out in darknet. In this paper, we propose a hierarchical classifier of darknet traffic which can distinguish four types of darknet(Tor, I2P, ZeroNet, Freenet) and 25 darknet users' behavior. Due to the lack of public datasets, we deployed a darknet data probe that can capture real darknet traffic in Tor, I2P, ZeroNet, Freenet. After collecting and labeling darknet traffic, we extract 26 time-based flow features that can represent the characteristics of darknet traffic and train a hierarchical classifier constructed by 6 local classifiers. Results show that the classifier can easily distinguish Tor, I2P, ZeroNet, Freenet four kinds of darknet clients with an accuracy of 96.9% and identify 8 kinds of user behaviors for each type of darknet with an accuracy of 91.6% on average. With the help of this hierarchical classification method, darknet user behaviors can be accurately distinguished at the traffic exit.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134510491","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: 15

Practical Secure Two-Party EdDSA Signature Generation with Key Protection and Applications in Cryptocurrency 具有密钥保护的实用安全两方EdDSA签名生成及其在加密货币中的应用

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00031

Qi Feng, D. He, Min Luo, Zengxiang Li, K. Choo

{"title":"Practical Secure Two-Party EdDSA Signature Generation with Key Protection and Applications in Cryptocurrency","authors":"Qi Feng, D. He, Min Luo, Zengxiang Li, K. Choo","doi":"10.1109/TrustCom50675.2020.00031","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00031","url":null,"abstract":"In cryptocurrency and blockchain-based distributed ledgers, transfer of money (digital coins) can be presented as a transaction. Due to the irreversibility nature of blockchain transactions, a single fraudulent use of private key (used to sign transactions) could have significant consequences (e.g. financial loss). Key protection alone is not adequate in protecting cryp-tocurrencies, and threshold signature is a viable method to avoid fraudulent key usage or key theft. In this paper, we focus on the Edwards-curve digital security algorithm (EdDSA), which has been applied in several cryptocurrencies (e.g. Cardano, Zcash, and Decred) and design the first efficient two-party EdDSA signing protocol. Unlike standard secret sharing, a valid signature is generated using an interactive protocol without the original key ever being exposed. We mathematically prove the security of our proposed protocol. Findings from the performance evalation of the protocol show that it achieves good performance for curve Ed25519, with a single signing operation in the malicious setting taking approximately 3.32 ms between two devices.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133372672","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

On the Usefulness of User Nudging and Strength Indication Concerning Unlock Pattern Security 论用户提示和强度指示对解锁模式安全的作用

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00227

Thomas Hupperich, Katharina Dassel

引用次数: 0

CPN Model Checking Method of Concurrent Software Based on State Space Pruning 基于状态空间剪枝的并发软件CPN模型检验方法

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00187

Tao Sun, Jing Yang, Wenjie Zhong

引用次数: 0

Inference Attacks on Physical Layer Channel State Information 基于物理层信道状态信息的推理攻击

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00125

Paul Walther, T. Strufe

引用次数: 3

A PHP and JSP Web Shell Detection System With Text Processing Based On Machine Learning 基于机器学习的文本处理Web Shell检测系统

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00219

Han Zhang, Ming Liu, Zihan Yue, Zhi Xue, Yong-yu Shi, Xiangjian He

引用次数: 4