2017 IEEE Trustcom/BigDataSE/ICESS最新文献_第9页

Ordol: Obfuscation-Resilient Detection of Libraries in Android Applications Ordol: Android应用程序中库的混淆弹性检测

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.292

Dennis Titze, Michael Lux, J. Schütte

引用次数: 6

SimiDroid: Identifying and Explaining Similarities in Android Apps SimiDroid:识别和解释Android应用程序的相似性

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.230

Li Li, Tegawendé F. Bissyandé, Jacques Klein

引用次数: 41

On the Performance of a Trustworthy Remote Entity in Comparison to Secure Multi-party Computation 可信远程实体与安全多方计算的性能比较

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.361

Robin Ankele, A. Simpson

{"title":"On the Performance of a Trustworthy Remote Entity in Comparison to Secure Multi-party Computation","authors":"Robin Ankele, A. Simpson","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.361","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.361","url":null,"abstract":"Novel trusted hardware extensions such as Intel's SGX enable user-space applications to be protected against potentially malicious operating systems. Moreover, SGX supports strong attestation guarantees, whereby remote parties can be convinced of the trustworthy nature of the executing user-space application. These developments are particularly interesting in the context of large-scale privacy-preserving data mining. In a typical data mining scenario, mutually distrustful parties have to share potentially sensitive data with an untrusted server, which in turn computes a data mining operation and returns the result to the clients. Generally, such collaborative tasks are referred to as secure multi-party computation (MPC) problems. Privacy-preserving distributed data mining has the additional requirement of (output) privacy preservation (which typically is achieved by the addition of random noise to the function output); additionally, it limits the general purpose functionality to distinct data mining operations. To solve these problems in a scalable and efficient manner, the concept of a Trustworthy Remote Entity (TRE) was recently introduced. We report upon the performance of a SGX-based TRE and compare our results to popular secure MPC frameworks. Due to limitations of the MPC frameworks, we benchmarked only simple operations (and argue that more complex data mining operations can be established by composing several basic operations). We consider both a two-party setting (where we iterate over the number of operations) and a multi-party setting (where we iterate over the number of participants).","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"17 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":"114109986","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

Detecting DNS Tunnel through Binary-Classification Based on Behavior Features 基于行为特征的二进制分类检测DNS隧道

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.256

Jingkun Liu, Shuhao Li, Yongzheng Zhang, Jun Xiao, Peng Chang, Chengwei Peng

{"title":"Detecting DNS Tunnel through Binary-Classification Based on Behavior Features","authors":"Jingkun Liu, Shuhao Li, Yongzheng Zhang, Jun Xiao, Peng Chang, Chengwei Peng","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.256","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.256","url":null,"abstract":"DNS tunnel is a typical Internet covert channel used by attackers or bots to evade the malicious activities detection. The stolen information is encoded and encapsulated into the DNS packets to transfer. Since DNS traffic is common, most of the firewalls directly allow it to pass and IDS does not trigger an alarm with it. The popular signature-based detection methods and threshold-based methods are not flexible and make high false alarms. The approaches based on characters distribution features also do not perform well, because attackers can modify the encoding method to disturb the characters distributions.In this paper, we propose an effective and applicable DNS tunnel detection mechanism. The prototype system is deployed at the Recursive DNS for tunnel identification. We use four kinds of features including time-interval features, request packet size features, record type features and subdomain entropy features. We evaluate the performance of our proposal with Support Vector Machine, Decision Tree and Logistical Regression. The experiments show that the method can achieve high detection accuracy of 99.96%.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"14 3 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":"123675391","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: 34

A Survey on Authorization in Distributed Systems: Information Storage, Data Retrieval and Trust Evaluation 分布式系统授权研究综述:信息存储、数据检索与信任评估

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.346

Ava Ahadipour, Martin Schanzenbach

{"title":"A Survey on Authorization in Distributed Systems: Information Storage, Data Retrieval and Trust Evaluation","authors":"Ava Ahadipour, Martin Schanzenbach","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.346","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.346","url":null,"abstract":"In distributed environments, entities are distributed among different security domains and they do not have prior knowledge of one another. In this setting, distributed systems and their security components such as entities, certificates, credentials, policies and trust values are dynamic and constantly changing. Thus, access control models and trust approaches are necessary to support the dynamic and distributed features of such systems and their components. The objective of this paper is to present a comprehensive survey about the security research in distributed systems. We have reviewed the dynamic and distributed nature of the components and evaluation methods of major authorization systems and access control models in existing literature. Based on this overview, we present a survey of selected trust schemes. We provide a categorization for recommendation-based and reputation-based trust models based on trust evaluation. Additionally, we use credential or certificate storage and chain discovery methods for categorizing evidencebased and policy-based trust models. This work can be used as a reference guide to understand authorization and trust management and to further research fully decentralized and distributed authorization systems.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"6 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":"122412578","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

WENC: HTTPS Encrypted Traffic Classification Using Weighted Ensemble Learning and Markov Chain 基于加权集成学习和马尔可夫链的HTTPS加密流量分类

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.219

Wubin Pan, Guang Cheng, Yongning Tang

{"title":"WENC: HTTPS Encrypted Traffic Classification Using Weighted Ensemble Learning and Markov Chain","authors":"Wubin Pan, Guang Cheng, Yongning Tang","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.219","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.219","url":null,"abstract":"SSL/TLS protocol is widely used for secure web applications (i.e., HTTPS). Classifying encrypted SSL/TLS based applications is an important but challenging task for network management. Traditional traffic classification methods are incapable of accomplishing this task. Several recently proposed approaches that focused on discriminating defining fingerprints among various SSL/TLS applications have also shown various limitations. In this paper, we design a Weighted ENsemble Classifier (WENC) to tackle these limitations. WENC studies the characteristics of various sub-flows during the HTTPS handshake process and the following data transmission period. To increase the fingerprint recognizability, we propose to establish a second-order Markov chain model with a fingerprint variable jointly considering the packet length and the message type during the process of HTTPS handshake. Furthermore, the series of the packet lengths of application data is modeled as HMM with optimal emission probability. Finally, a weighted ensemble strategy is devised to accommodate the advantages of several approaches as a unified one. Experimental results show that the classification accuracy of the proposed method reaches 90%, with an 11% improvement on average comparing to the state-of-the-art methods.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"24 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":"121041624","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: 21

Privileged Data Within Digital Evidence 数字证据中的特权数据

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.307

Dominique Fleurbaaij, M. Scanlon, Nhien-An Le-Khac

{"title":"Privileged Data Within Digital Evidence","authors":"Dominique Fleurbaaij, M. Scanlon, Nhien-An Le-Khac","doi":"10.1109/Trustcom/BigDataSE/ICESS.2017.307","DOIUrl":"https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.307","url":null,"abstract":"In recent years the use of digital communication has increased. This also increased the chance to find privileged data in the digital evidence. Privileged data is protected by law from viewing by anyone other than the client. It is up to the digital investigator to handle this privileged data properly without being able to view the contents. Procedures on handling this information are available, but do not provide any practical information nor is it known how effective filtering is. The objective of this paper is to describe the handling of privileged data in the current digital forensic tools and the creation of a script within the digital forensic tool Nuix. The script automates the handling of privileged data to minimize the exposure of the contents to the digital investigator. The script also utilizes technology within Nuix that extends the automated search of identical privileged document to relate files based on their contents. A comparison of the 'traditional' ways of filtering within the digital forensic tools and the script written in Nuix showed that digital forensic tools are still limited when used on privileged data. The script manages to increase the effectiveness as direct result of the use of relations based on file content.","PeriodicalId":170253,"journal":{"name":"2017 IEEE Trustcom/BigDataSE/ICESS","volume":"5 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":"128303956","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: 4

An Eclat Algorithm Based Energy Detection for Cognitive Radio Networks 基于Eclat算法的认知无线网络能量检测

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.358

Fan Jin, V. Varadharajan, U. Tupakula

引用次数: 4

Grouping-Proofs Based Access Control Using KP-ABE for IoT Applications 使用KP-ABE的基于组证明的物联网应用访问控制

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.251

Lyes Touati

引用次数: 2

Detection of Single Event Transients Based on Compressed Sensing 基于压缩感知的单事件瞬态检测

2017 IEEE Trustcom/BigDataSE/ICESS Pub Date : 2017-08-01 DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.223

C. Shao, Huiyun Li

引用次数: 0