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Preventing Recommendation Attack in Trust-Based Recommender Systems

Journal of Computer Science & Technology 2011年第5期26卷 823-828页

作者：张富国 School of Information and Technology Jiangxi University of Finance and Economics Institute of Information Resource Management Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation *** work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these *** argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender *** the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender *** analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the ***,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender *** study of the defend method is done with the dataset crawled from Epinions website.

关键词： data lineage ＂victim＂ node attack trust propagation

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Improving performance by creating a native join-index for OLAP

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Frontiers of Computer Science 2011年第2期5卷 236-249页

作者： Yansong ZHANG Shan WANG Jiaheng LU National Survey Research Center at Renmin University of China Beijing 100872 China Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

The performance of online analytical processing （OLAP） is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high perfor- mance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation： main- memory database based OLAP （MMOLAP） cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing （DDTA） based join （DDTA- JOIN）, a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.

关键词： directly dimensional tuple accessing (DDTA) – DDTA JOIN – native join index – predicate tree

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Safe side effects commitment for OS-level virtualization 11

Safe side effects commitment for OS-level virtualization

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Proceedings of the 8th ACM international conference on Autonomic computing

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

ISBN: (纸本)9781450306072

A common application of virtual machines (VM) is to use and then throw away, basically treating a VM like a completely isolated and disposable entity. The disadvantage of this approach is that if there is no malicious activity, the user has to re-do all of the work in her actual workspace since there is no easy way to commit (i.e., merge) only the benign updates within the VM back to the host environment. In this work, we develop a VM commitment system called Secom to automatically eliminate malicious state changes when merging the contents of an OS-level VM to the host. Secom consists of three steps: grouping state changes into clusters, distinguishing between benign and malicious clusters, and committing benign clusters. Secom has three novel features. First, instead of relying on a huge volume of log data, it leverages OS-level information flow and malware behavior information to recognize malicious changes. As a result, the approach imposes a smaller performance overhead. Second, different from existing intrusion detection and recovery systems that detect compromised OS objects one by one, Secom classifies objects into clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the false positive rate when identifying malicious clusters, it simultaneously considers two malware behaviors that are of different types and the origin of the processes that exhibit these behaviors, rather than considers a single behavior alone as done by existing malware detection methods. We have successfully implemented Secom on the Feather-weight Virtual Machine (FVM) system, a Windows-based OS-level virtualization system. Experiments show that the prototype can effectively eliminate malicious state changes while committing a VM with small performance degradation. Moreover, compared with the commercial anti-malware tools, the Secom prototype has a smaller number of false negatives and thus can more thoroughly clean up malware side effects. In addit

关键词： Virtual machine

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Effect factors on secondary structure of protein sequence pattern

Effect factors on secondary structure of protein sequence pa...

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International Workshop on Intelligent Systems and Applications

作者： Liu, Tao Li, Minghui Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9781424498574

Discovering the relationship between protein sequence pattern and protein secondary structure is important for accurately predicting secondary structure of protein sequence. A protein secondary structure pattern dictionary is constructed using protein sequence pattern and its corresponding secondary structure in this paper. Based on the constructed dictionary, we propose four effect factors on secondary structure of protein sequence pattern, including 1) the core pattern itself;2) patterns or amino acid residues that neighbor with the core pattern;3) patterns or amino acid residues that are far away from the core pattern;and 4) amino acid sequence segment that match the core pattern. Statistical measures are adopted to analyze these factors. The experimental result shows the reliability of these factors. The recognition of these effect factors presents new directions to predict protein secondary structure based on protein pattern dictionary. © 2011 IEEE.

关键词： Amino acids

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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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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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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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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 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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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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