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Proceedings of the 2011 ACM Symposium on Applied Computing

作者： Shan, Zhiyong Chiueh, Tzi-Cker Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China Stony Brook University United States

ISBN: (纸本)9781450301138

OS-level virtualization incurs smaller start-up and run-time overhead than HAL-based virtualization and thus forms an important building block for developing fault-tolerant and intrusion-tolerant applications. A complete implementation of OS-level virtualization on the Windows platform requires virtualization of Windows services, such as system services like the Remote Procedure Call Server Service (RPCSS), because they are essentially extensions of the kernel. As Windows system services work very differently from their counterparts on UNIX-style OS, i.e., daemons, and many of their implementation details are proprietary, virtualizing Windows system services turned out to be the most challenging technical barrier for OS-level virtualization for the Windows platform. In this paper, we describe a general technique to virtualize Windows services, and demonstrate its effectiveness by applying it to successfully virtualize a set of important Windows system services and ordinary services on different versions of Windows OS, including RPCSS, DcomLaunch, IIS service group, Tlntsvr, MySQL, Apache2.2, CiSvc, ImapiService, etc. © 2011 ACM.

关键词： Virtual machine

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Efficient Duplicate Detection on Cloud Using a New Signature Scheme

Efficient Duplicate Detection on Cloud Using a New Signature...

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12th International Conference on Web-Age Information Management

作者： Rong, Chuitian Lu, Wei Du, Xiaoyong Zhang, Xiao Key Labs. of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9783642235344;9783642235351

Duplicate detection has been well recognized as a crucial task to improve the quality of data. Related work on this problem mainly aims to propose efficient approaches over a single machine. However, with increasing volume of the data, the performance to identify duplicates is still far from satisfactory. Hence, we try to handle the problem of duplicate detection over MapReduce, a share-nothing paradigm. We argue the performance of utilizing MapReduce to detect duplicates mainly depends on the number of candidate record pairs. In this paper, we proposed a new signature scheme with new pruning strategy over MapReduce to minimize the number of candidate record pairs. Our experimental results over both real and synthetic datasets demonstrate that our proposed signature based method is efficient and scalable.

关键词： duplicate detection MapReduce Cloud

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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.Key laboratory of data engineering and knowledge engineering,Ministry of Education,Beijing 100872,China 2.School of Information,Renmin University of China,Beijing 100872,China 3.Institute of Electronic Technology,Information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

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Tracer: Enforcing Mandatory Access Control in commodity OS with the support of light-weight intrusion detection and tracing 11

Tracer: Enforcing Mandatory Access Control in commodity OS w...

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6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011

作者： Shan, Zhiyong Wang, Xin Chiueh, Tzi-Cker Key Laboratory of Data Engineering and Knowledge Engineering Renmin University MOE China Stony Brook University United States Industrial Technology Research Institute Taiwan

ISBN: (纸本)9781450305648

Enforcing a practical Mandatory Access Control (MAC) in a commercial operating system to tackle malware problem is a grand challenge but also a promising approach. The firmest barriers to apply MAC to defeat malware programs are the incompatible and unusable problems in existing MAC systems. To address these issues, we start our work by analyzing the technical details of 2,600 malware samples one by one and performing experiments over two types of MAC enforced operating systems. Based on the preliminary studies, we design a novel MAC model incorporating intrusion detection and tracing in a commercial operating system, named Tracer, in order to disable malware on hosts while offering good compatibility to existing software and good usability to common users who are not system experts. The model conceptually consists of three actions: detecting, tracing and restricting suspected intruders. One novelty is that it leverages light-weight intrusion detection and tracing techniques to automate security label configuration that is widely acknowledged as a tough issue when applying a MAC system in practice. The other is that, rather than restricting information flow as a traditional MAC does, it traces intruders and restricts only their critical malware behaviors, where intruders represent processes and executables that are potential agents of a remote attacker. Our prototyping and experiments on Windows show that Tracer can effectively defeat all malware samples tested via blocking malware behaviors while not causing a significant compatibility problem. Copyright 2011 ACM.

关键词： Malware

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Scalable process modeling based on net synthesis under cloud computing environment

Scalable process modeling based on net synthesis under cloud...

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zhou, Jiantao Liang, Rong Wang, Yan College of Computer Science Inner Mongolia University Hohhot China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Recently research on modeling methods of complicated processes under complex network environments has become a focus in workflow field. Now cloud computing environment provides a specific application background for the interaction and collaboration of workflows. In this paper, workflow processes were modeled as Semantics Constrained Net (SCN), which is one kind of extended Petri net. Then net synthesis mechanism constrained by semantics was researched, including synthesis rules and liveness proven. And a scalable solution of complex workflow process modeling in cloud was proposed. An instance of e-business was given to describe cooperative workflow under cloud computing environment. © 2011 IEEE.

关键词： Semantics

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Detecting stealthy malware with inter-structure and imported signatures 11

Detecting stealthy malware with inter-structure and imported...

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6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011

作者： Liang, Bin You, Wei Shi, Wenchang Liang, Zhaohui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781450305648

Recent years have witnessed an increasing threat from kernel rootkits. A common feature of such attack is hiding malicious objects to conceal their presence, including processes, sockets, and kernel modules. Scanning memory with object signatures to detect the stealthy rootkit has been proven to be a powerful approach only when it is hard for adversaries to evade. However, it is difficult, if not impossible, to select fields from a single data structure as robust signatures with traditional techniques. In this paper, we propose the concepts of inter-structure signature and imported signature, and present techniques to detect stealthy malware based on these concepts. The key idea is to use cross-reference relationships of multiple data structures as signatures to detect stealthy malware, and to import some extra information into regions attached to target data structures as signatures. We have inferred four invariants as signatures to detect hidden processes, sockets, and kernel modules in Linux respectively and implemented a prototype detection system called DeepScanner. Meanwhile, we have also developed a hypervisor-based monitor to protect imported signatures. Our experimental result shows that our DeepScanner can effectively and efficiently detect stealthy objects hidden by seven real-world rootkits without any false positives and false negatives, and an adversary can hardly evade DeepScanner if he/she does not break the normal functions of target objects and the system. Copyright 2011 ACM.

关键词： Malware

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Random rough subspace based neural network ensemble for insurance fraud detection

Random rough subspace based neural network ensemble for insu...

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International Workshop on Computational Transportation Science

作者： Xu, Wei Wang, Shengnan Zhang, Dailing Yang, Bo Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769543352

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. © 2011 IEEE.

关键词： Insurance

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A neural network based forecasting method for the unemployment rate prediction using the search engine query data

A neural network based forecasting method for the unemployme...

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2011 8th IEEE International Conference on e-Business engineering, ICEBE 2011

作者： Xu, Wei Zheng, Tingting Li, Ziang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769545189

Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. © 2011 IEEE.

关键词： Forecasting

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

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IEEE International Conference on Computer Science and Automation engineering

作者： Han, Hong-Qi An, Xiaomi Zhu, Donghua Wang, Xuefeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Management and Economics Beijing Institute of Technology Beijing 100872 China

ISBN: (纸本)9781424487257

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value;LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method. © 2011 IEEE.

关键词： Visualization

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A RFID mixed coding scheme based on huffman algorithm

A RFID mixed coding scheme based on huffman algorithm

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Jian, Jiang Guoqiong, Liao Hao, Wang Qizhi, Wan School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang Nanchang China

ISBN: (纸本)9780769545448

In RFID application systems with multiple packaging layers, labeling packaging relationship of objects in different packaging layers by encoding methods is a important technology field. Prefix-based labeling scheme is a method by which the packaging relationship of objects amounts to testing whether one object is an prefix of the other, whereas the region numbering scheme is a method by which an ancestor query amount to an interval containment test on the labels. This paper first presents a mixed-encoding scheme based on Huffman algorithm, then propose a variant of this encoding scheme in which new "virtual nodes" are involved in order to improve the stability of encoding. Our experiments prove that the mixed-based encoding scheme is effective due to the enhancement of update performance and query efficiency. © 2011 IEEE.

关键词： Encoding (symbols)

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