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

A Novel Method Makes Concolic System More Effective 一种新的方法使圆锥系统更有效

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.43

Hongliang Liang, Zhengyu Li, Minhuan Huang, Xiaoxiao Pei

引用次数: 0

Machine Learning Based DDoS Attack Detection from Source Side in Cloud 基于机器学习的云源端DDoS攻击检测

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.58

Zecheng He, Tianwei Zhang, R. Lee

{"title":"Machine Learning Based DDoS Attack Detection from Source Side in Cloud","authors":"Zecheng He, Tianwei Zhang, R. Lee","doi":"10.1109/CSCloud.2017.58","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.58","url":null,"abstract":"Denial of service (DOS) attacks are a serious threat to network security. These attacks are often sourced from virtual machines in the cloud, rather than from the attacker's own machine, to achieve anonymity and higher network bandwidth. Past research focused on analyzing traffic on the destination (victim's) side with predefined thresholds. These approaches have significant disadvantages. They are only passive defenses after the attack, they cannot use the outbound statistical features of attacks, and it is hard to trace back to the attacker with these approaches. In this paper, we propose a DOS attack detection system on the source side in the cloud, based on machine learning techniques. This system leverages statistical information from both the cloud server's hypervisor and the virtual machines, to prevent network packages from being sent out to the outside network. We evaluate nine machine learning algorithms and carefully compare their performance. Our experimental results show that more than 99.7% of four kinds of DOS attacks are successfully detected. Our approach does not degrade performance and can be easily extended to broader DOS attacks.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"21 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":"121504145","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: 118