2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)最新文献_第5页

Security Analysis of Email Systems 电子邮件系统安全分析

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.20

Tianlin Li, Amish Mehta, Ping Yang

引用次数: 9

Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging 基于K-NN和社会标签的隐私保护和安全推荐系统

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.24

R. Katarya, O. Verma

{"title":"Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging","authors":"R. Katarya, O. Verma","doi":"10.1109/CSCloud.2017.24","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.24","url":null,"abstract":"With the introduction of Web 2.0, there has been an extreme increase in the popularity of social bookmarking systems and folksonomies. In this paper, our motive is to develop a recommender system that is based on user assigned tags and content present on web pages. Although the tag recommendations in social tagging systems can be very accurate and personalized, there exists an issue of risk to the privacy of user's profile, since the social tags are given by a user expose his preferences to other users in contact. To overcome this problem, we have incorporated obfuscation privacy strategies with the well-known Delicious dataset in social tagging based recommender system. We have applied the popular supervised machine-learning algorithm, K-Nearest Neighbours classifier to the dataset that recommends relevant tags to the user. Privacy has been introduced in our tag-based recommender system by hiding some of the necessary tags, bookmarks of a user and replacing them with some random tags and bookmarks. Our experiment results indicate that the recommender system being implemented is highly efficient in terms recall and privacy measure for different values of k. The results and comparisons indicate that we have successfully employed an effective tag recommender system, which also protects the user's privacy without any significant fall in the quality of recommendation.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133319131","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

Family Relationship Inference Using Knights Landing Platform 基于骑士登陆平台的家庭关系推断

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.41

Yuxiang Gao, Wei-Min Chen

{"title":"Family Relationship Inference Using Knights Landing Platform","authors":"Yuxiang Gao, Wei-Min Chen","doi":"10.1109/CSCloud.2017.41","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.41","url":null,"abstract":"Using genetic data to infer relatedness has been crucial for genetics studies for decades. In a previously published paper together with the KING software, we demonstrated that the kinship coefficient, a measure of relatedness between a pair of individuals, can be accurately estimated using their genome-wide SNP data, without estimating the allele frequencies at each SNP in the whole dataset. The computational efficiency of this algorithm has been substantially improved in the second generation of KING. Three levels of computational speed-up are implemented in KING 2.0, including: 1) bit-level parallelism; 2) multiple-core parallelism using OpenMP; and 3) a multi-stage procedure to eliminate unrelated or distantly related pairs of individuals. The efficient implementation in KING 2.0 allows instant relationship inference in a matter of seconds in a typical dataset (with 10,000s individuals). To demonstrate the computational performance and scalability of KING 2.0, we use the Knights Landing platform to infer relatedness in a dataset consisting of 303,750 individuals each typed at 168,749 autosome SNPs. The computational time to identify all first-degree relatives by scanning 46 billion pairs of individuals is ∼10 minutes using 256 threads, a noticeable speed-up comparing to the general-purpose CPU. Algorithm improvement in the second generation of KING and the use of the latest computing system such as the Knights Landing platform makes it feasible for researchers to infer relatedness in their genetic datasets in the largest size up-to-date on a single computer.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794659","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

Power and Performance Study of HPC Applications on QCT Developer Cloud 基于QCT开发者云的高性能计算应用功耗与性能研究

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.50

P. Young, Probha Madhavan, Gong-Do Hwang

引用次数: 0

End-to-End IoT Security Middleware for Cloud-Fog Communication 云雾通信的端到端物联网安全中间件

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.62

B. Mukherjee, R. Neupane, P. Calyam

引用次数: 47

mPSI: Many-to-one Private Set Intersection mPSI:多对一私有集合交集

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.35

Keji Hu, Wensheng Zhang

引用次数: 0

Email Visualization Correlation Analysis Forensics Research 电子邮件可视化关联分析取证研究

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.28

Zhenya Chen, Ying Yang, Lijuan Chen, Liqiang Wen, Jizhi Wang, Guang Yang, Meng Guo

引用次数: 4

Design of Virtualization Framework to Detect Cyber Threats in Linux Environment Linux环境下网络威胁检测的虚拟化框架设计

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.18

D. Levy, Hardik A. Gohel, Himanshu Upadhyay, A. Perez-Pons, Leonel E. Lagos

{"title":"Design of Virtualization Framework to Detect Cyber Threats in Linux Environment","authors":"D. Levy, Hardik A. Gohel, Himanshu Upadhyay, A. Perez-Pons, Leonel E. Lagos","doi":"10.1109/CSCloud.2017.18","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.18","url":null,"abstract":"In today's software and systems environments, security frameworks and models are evolving exponentially. Many traditional host-based frameworks are currently available to detect cyber threats in Linux environment. But there have been many challenges in detecting rootkits that modify the Linux Operating System (OS) kernel to avoid detection. These limitations have lead us to design a virtualization framework for detection of cyber threats in Linux environment. Instead of relying on the Linux Operating System kernel which is now a common victim of cyber-attacks, this virtualization framework will rely on the virtual machine hypervisor which is a more secure software layer that runs the OS kernel and the hardware. The paper proposed a virtualization framework based on well-known hypervisors, to detect cyber threats. The proposed work allowed for a more robust cyber threat detection method than traditional host-based frameworks. It can also possess self-healing properties since it will not only detect compromised servers but also suspend their operation by replacing them with uncompromised versions. This innovative framework promises to secure large scale IT infrastructure with minimum maintenance cost.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114264374","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: 5

Network Anomaly Detection with Stochastically Improved Autoencoder Based Models 基于随机改进自编码器模型的网络异常检测

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.39

R. C. Aygun, A. Yavuz

引用次数: 107

Malware Fingerprinting under Uncertainty 不确定性下的恶意软件指纹识别

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.63

Krishnendu Ghosh, W. Casey, J. Morales, B. Mishra

{"title":"Malware Fingerprinting under Uncertainty","authors":"Krishnendu Ghosh, W. Casey, J. Morales, B. Mishra","doi":"10.1109/CSCloud.2017.63","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.63","url":null,"abstract":"Malware detection and classification is critical for the security of IT infrastructure. Legacy detection of malware has been highly reliant on static signatures, so malware authors have evolved code polymorphic techniques to counteract these tools, thus rendering static malware detectors ineffective. While malware writers may easily use code rewriting techniques to scramble binary images; malware processes at runtime still must conduct a sequence of operational steps to achieve its design goal, indicating an approach based on behavioral analysis where the captured invariants form a new type of forensic fingerprint. Moreover these operational steps are constrained to occur within the computers' or mobile devices' abstract system interface - a finite basis of activities that submit to effective monitoring with a variety of tools. In this work, we propose a formalism for expressing these behaviors, learning them and analyzing them to form automated malware analysis tools. Thus motivated by a need to detect and classify malware, we root its foundation in formal verification, as well as methodology from statistical and machine learning. Specifically using trace data from malware we leverage formal verification methods (such as probabilistic model checking) to construct classifiers and evaluate their efficacy in supervised learning and cross-fold validation experiments. The results inform how a fully automated reasoning mechanism may be applied to unknown software by posing its system trace as a query to various classifiers as hypothesis testing, the outputs informing belief of membership. Finally, we demonstrate the method and results on real malware data.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129421526","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