2017 IEEE Trustcom/BigDataSE/ICESS最新文献_第4页

Anonymous CoinJoin Transactions with Arbitrary Values 具有任意值的匿名CoinJoin事务

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.280

F. Maurer, Till Neudecker, Martin Florian

{"title":"Anonymous CoinJoin Transactions with Arbitrary Values","authors":"F. Maurer, Till Neudecker, Martin Florian","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.280","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.280","url":null,"abstract":"Bitcoin, the arguably most popular cryptocurrency to date, allows users to perform transactions using freely chosen pseudonymous addresses. Previous research, however, suggests that these pseudonyms can easily be linked, implying a lower level of privacy than originally expected. To obfuscate the links between pseudonyms, different mixing methods have been proposed. One of the first approaches is the CoinJoin concept, where multiple users merge their transactions into one larger transaction. In theory, CoinJoin can be used to mix and transact bitcoins simultaneously, in one step. Yet, it is expected that differing bitcoin amounts would allow an attacker to derive the original single transactions. Solutions based on CoinJoin therefore prescribe the use of fixed bitcoin amounts and cannot be used to perform arbitrary transactions.In this paper, we define a model for CoinJoin transactions and metrics that allow conclusions about the provided anonymity. We generate and analyze CoinJoin transactions and show that with differing, representative amounts they generally do not provide any significant anonymity gains. As a solution to this problem, we present an output splitting approach that introduces sufficient ambiguity to effectively prevent linking in CoinJoin transactions. Furthermore, we discuss how this approach could be used in Bitcoin today.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527979","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: 40

Precision-Enhanced Image Attribute Prediction Model 精度增强图像属性预测模型

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.324

Chen Hu, J. Miao, Zhuo Su, X. Shi, Qiang Chen, Xiaonan Luo

引用次数: 2

Discovering Malicious Domains through Alias-Canonical Graph 利用别名-规范图发现恶意域

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.241

Chengwei Peng, Xiao-chun Yun, Yongzheng Zhang, Shuhao Li, Jun Xiao

{"title":"Discovering Malicious Domains through Alias-Canonical Graph","authors":"Chengwei Peng, Xiao-chun Yun, Yongzheng Zhang, Shuhao Li, Jun Xiao","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.241","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.241","url":null,"abstract":"Malicious domains play a vital component in various cyber crimes. Most of the prior works depend on DNS A (address) records to detect the malicious domains, which are directly resolved to IP addresses. In this paper, we propose a malicious domain detection method focusing on the domains that are not resolved to IP addresses directly but only appear in DNS CNAME (canonical name) records. This kind of domains occupy 18.39% of the total domains in our 1530-days-long DNS traffic dataset collected from 217 DNS servers. In addition, the real-world dataset shows that domains connected with malicious ones through DNS CNAME records tend to be malicious too. Based on this observation, our proposal can identify the illegal domains by computing their maliciousness probabilities. The experiments demonstrate the high detection performance of our solution. It achieves the accuracy, on average, over 97.25% true positive rate with less than 0.027% false positive rate. Moreover, the proposal performs near real time detections. Our work can help network attack defenders to build a more robust domain monitoring system.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125534257","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

A Methodology for Privacy-Aware IoT-Forensics 隐私感知物联网取证方法

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/TRUSTCOM/BIGDATASE/ICESS.2017.293

Ana Nieto, Ruben Rios, Javier López

引用次数: 33

Detecting Permission Over-claim of Android Applications with Static and Semantic Analysis Approach 基于静态和语义分析方法的Android应用权限过度检测

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.303

Junwei Tang, Ruixuan Li, Hongmu Han, Heng Zhang, X. Gu

{"title":"Detecting Permission Over-claim of Android Applications with Static and Semantic Analysis Approach","authors":"Junwei Tang, Ruixuan Li, Hongmu Han, Heng Zhang, X. Gu","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.303","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.303","url":null,"abstract":"Android access control granularity based on its permission mechanism is relatively coarse, which cannot effectively protect the user privacy. Many Android applications do not strictly abide by the principle of least privilege (PLP). Both benign and malicious apps may request more permissions than those they really use. We rethink previous permission over-claim problem of Android applications, and extend it to three kinds of problems: Explicit Permission Over-claim, Implicit Permission Over-claim and Ad Library Permission Over-claim. The latter two problems are new that have not been raised by any previous work. Static analysis is to decompile the applications to generate intermediate code and then analyze the usage of permissions. Our static analysis on 10710 applications shows that 76.08% of them may have Explicit Permission Over-claim problem, among those there are 424 applications that have sensitive permissions, which are only used in the advertisement library’s code of the applications rather than developer’s own code. They have Ad Library Permission Over-claim problem. The main idea of our semantic analysis is to calculate the semantic similarity between apps’ descriptions and function phrases. If the similarity exceeds a certain threshold, the app is considered relevant to the corresponding function. We compare the results of the semantic analysis with those of manual reading of 102 Android application descriptions. The F-measures of the three chosen functions are 80.82%, 70.48% and 89.62%, respectively. The evaluation results show our method can efficiently detect the above three kinds of permission over claim problems which indicates that our method would be helpful for normal users to have a clear understanding of permission usage of Android applications.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133591372","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: 11

HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems HiNextApp:移动系统中应用预测的上下文感知和自适应框架

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.312

Chaoneng Xiang, Duo Liu, Shiming Li, Xiao Zhu, Yang Li, Jinting Ren, Liang Liang

{"title":"HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems","authors":"Chaoneng Xiang, Duo Liu, Shiming Li, Xiao Zhu, Yang Li, Jinting Ren, Liang Liang","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.312","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.312","url":null,"abstract":"A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed several methods of predicting next used Apps (here in after appprediction) to solve the issues, but faces the problems of the low prediction accuracy and high training costs. Especially, applying app-prediction to memory management (such as LMK) and App prelaunching has high requirements for the prediction accuracy and training costs. In this paper, we propose an app-prediction framework, named HiNextApp, to improve the app-prediction accuracy and reduce training costs in mobile systems. HiNextApp is based on contextual information, and can adjust the size of prediction periods adaptively. The framework mainly consists of two parts: non-uniform bayes model and an elastic algorithm. The experimental results show that HiNextApp can effectively improve the prediction accuracy and reduce training times. Besides, compared with traditional bayes model, the overhead of our framework is relatively low.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922501","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: 11

An Intrusion Detection System Based on Polynomial Feature Correlation Analysis 基于多项式特征相关分析的入侵检测系统

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.340

Qingru Li, Zhiyuan Tan, Aruna Jamdagni, P. Nanda, Xiangjian He, Wei Han

引用次数: 13

Implementing A Framework for Big Data Anonymity and Analytics Access Control 实现大数据匿名和分析访问控制框架

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.325

Mohammed Al-Zobbi, S. Shahrestani, Chun Ruan

{"title":"Implementing A Framework for Big Data Anonymity and Analytics Access Control","authors":"Mohammed Al-Zobbi, S. Shahrestani, Chun Ruan","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.325","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.325","url":null,"abstract":"Analytics in big data is maturing and moving towards mass adoption. The emergence of analytics increases the need for innovative tools and methodologies to protect data against privacy violation. Many data anonymization methods were proposed to provide some degree of privacy protection by applying data suppression and other distortion techniques. However, currently available methods suffer from poor scalability, performance and lack of framework standardization. Current anonymization methods are unable to cope with the massive size of data processing. Some of these methods were especially proposed for MapReduce framework to operate in Big Data. However, they still operate in conventional data management approaches. Therefore, there were no remarkable gains in the performance. We introduce a framework that can operate in MapReduce environment to benefit from its advantages, as well as from those in Hadoop ecosystems. Our framework provides a granular user's access that can be tuned to different authorization levels. The proposed solution provides a fine-grained alteration based on the user's authorization level to access MapReduce domain for analytics. Using well-developed role-based access control approaches, this framework is capable of assigning roles to users and map them to relevant data attributes.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129468200","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

ProtectCall: Call Protection Based on User Reputation ProtectCall:基于用户信誉的呼叫保护

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.297

Ibrahim Tariq Javed, Khalifa Toumi, N. Crespi

引用次数: 1

Implementing Usage Control in Internet of Things: A Smart Home Use Case 在物联网中实现使用控制:一个智能家居用例

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.352

Antonio La Marra, F. Martinelli, P. Mori, A. Saracino

引用次数: 44