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Detecting Compromised Kernel Hooks with Support of Hardware Debugging Features

China Communications 2012年第10期9卷 78-90页

作者： Shi Wenchang Zhou HongWei Yuan JinHui Liang Bin School of Information Renmin University of China Beijing 100872 P. R. China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872 P. R. China Information Engineering University of China Zhengzhou 450004 P. R. China

Although there exist a few good schemes to protect the kernel hooks of operating systems, attackers are still able to circumvent existing defense mechanisms with spurious context infonmtion. To address this challenge, this paper proposes a framework, called HooklMA, to detect compromised kernel hooks by using hardware debugging features. The key contribution of the work is that context information is captured from hardware instead of from relatively vulnerable kernel data. Using commodity hardware, a proof-of-concept pro- totype system of HooklMA has been developed. This prototype handles 3 082 dynamic control-flow transfers with related hooks in the kernel space. Experiments show that HooklMA is capable of detecting compomised kernel hooks caused by kernel rootkits. Performance evaluations with UnixBench indicate that runtirre overhead introduced by HooklMA is about 21.5%.

关键词： operating system kernel hook integrity hardware control flow

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An Approach for Personalized Tag Recommendation Based on Interest Transfer Model

An Approach for Personalized Tag Recommendation Based on Int...

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Web Information Systems and Applications Conference (WISA)

作者： Yue Liu Nan Yang Gang Yang The school of Information Renmin University of China Bejing China The school of Information Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Bejing China

Recently, social tagging systems become more and more popular in many Web 2.0 applications. In such systems, Users are allowed to annotate a particular resource with a freely chosen a set of tags. These user-generated tags can represent users' interests more concise and closer to human understanding. Interests will change over time. Thus, how to describe users' interests and interests transfer path become a big challenge for personalized recommendation systems. In this approach, we propose a variable-length time interval division algorithm and user interest model based on time interval. Then, in order to draw users' interests transfer path over a specific time period, we suggest interest transfer model. After that, we apply a classical community partition algorithm in our approach to separate users into communities. Finally, we raise a novel method to measure users' similarities based on interest transfer model and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our approach than classical user-based collaborative filtering methods.

关键词： Mathematical model Computational modeling Collaboration Educational institutions Tagging Filtering Heuristic algorithms

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LotusX: A Position-Aware XML Graphical Search System with Auto-Completion

LotusX: A Position-Aware XML Graphical Search System with Au...

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International Conference on data engineering

作者： Chunbin Lin Jiaheng Lu Tok Wang Ling Bogdan Cautis Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Computing National University of Singapore Singapore Télécom Paris Technology France

ISBN: (纸本)9781467300421

The existing query languages for XML (e.g., XQuery) require professional programming skills to be formulated, however, such complex query languages burden the query processing. In addition, when issuing an XML query, users are required to be familiar with the content (including the structural and textual information) of the hierarchical XML, which is diffcult for common users. The need for designing user friendly interfaces to reduce the burden of query formulation is fundamental to the spreading of XML community. We present a twig-based XML graphical search system, called LotusX, that provides a graphical interface to simplify the query processing without the need of learning query language and data schemas and the knowledge of the content of the XML document. The basic idea is that LotusX proposes "position-aware" and "auto-completion" features to help users to create tree-modeled queries (twig pattern) by providing the possible candidates on-the-fly. In addition, complex twig queries (including order sensitive queries) are supported in LotusX. Furthermore, a new ranking strategy and a query rewriting solution are implemented to rank and rewrite the query effectively. We provide an online demo for LotusX system: http://***:8080/LotusX.

关键词： XML database languages Indexes Books Query processing Programming keyword search

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Optimizing parameters of algorithm trading strategies using MapReduce

Optimizing parameters of algorithm trading strategies using ...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiongpai Qin Huiju Wang Furong Li Jidong Chen Xuan Zhou Xiaoyong Du Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering (RUC) Ministry of Education Beijing China School of Information Renmin University of China Beijing China EMC-Greenplum Research China China

In algorithm trading, computer algorithms are used to make the decision on the time, quantity, and direction of operations (buy, sell, or hold) automatically. To create a useful algorithm, the parameters of the algorithm should be optimized based on historical data. However, Parameter optimization is a time consuming task, due to the large search space. We propose to search the parameter combination space using the MapReduce framework, with the expectation that runtime of optimization be cut down by leveraging the parallel processing capability of MapReduce. This paper presents the details of our method and some experiment results to demonstrate its efficiency. We also show that a rule based strategy after being optimized performs better in terms of stability than the one whose parameters are arbitrarily preset, while making a comparable profit.

关键词： Optimization Measurement Computers Algorithm design and analysis Runtime Servers data processing

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Single attribute Join Queries within latest sampling periods in sensor networks

Single attribute Join Queries within latest sampling periods...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Shangfeng Mo Hong Chen Yinglong Li School of Math and Computing Science Hunan University of Science and Technology Xiangtan China Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China

Join processing in wireless sensor networks is a challenging problem. Current solutions are not involved in the join operation among tuples of the latest sampling periods. In this article, we proposed a continuous Single attribute Join Queries within latest sampling Periods (SJQP) for wireless sensor networks. The main idea of our filter-based framework is to discard non-matching tuples, and our scheme can guarantee the result is correct independent of the filters. Experiments based on real-world sensor data show that our method performs close to a theoretical optimum and consistently outperforms the centralized join algorithm.

关键词： Base stations Wireless sensor networks Probes Filtering algorithms Temperature sensors Information filters

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Reducing Uncertainty of Low-Sampling-Rate Trajectories

Reducing Uncertainty of Low-Sampling-Rate Trajectories

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International Conference on data engineering

作者： Kai Zheng Yu Zheng Xing Xie Xiaofang Zhou School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Microsoft Research Asia Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781467300421

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.

关键词： Trajectory Roads Global Positioning System Inference algorithms Uncertainty Artificial neural networks Educational institutions

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Drivers for strategic choice of cloud computing as online service in SMEs

Drivers for strategic choice of cloud computing as online se...

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International Conference on Information Systems, ICIS 2012

作者： Li, Min Yu, Yan Zhao, J. Leon Li, Xin University of Science and Technology China-City University of Hong Kong Joint Advanced Research Center 166 Renai Road Dushu Lake Higher Education Town Suzhou China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China 59 Zhongguancun Street Haidian District Beijing China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)9781627486040

Cloud Computing Service (CCS) paradigm is changing IT strategy of organizations in the digital world. CCS that requires few upfront investments and uses lease-based pricing is especially relevant to the Small and Medium Enterprises (SMEs), which have limited resources and may not know their true valuation for the IT prior to adoption. Thus, this research aims to investigate the influential factors of SMEs' strategic choice of CCS as online service. Relying upon Technology-Organization-Environment (TOE) paradigm, we identify both generic and context-specific factors from the three aspects and explain how the identified factors affect SMEs' CCS strategic choices. We hope this research can make contributions to innovation diffusion theory and IT strategy literature. We also hope the research with progress going on can generate insights for the CCS vendors who care about the sector of SME as well as the government administrators to make appropriate policies or supports for SMEs.

关键词： Cloud computing

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Preventing Recommendation Attack in Trust-Based Recommender Systems

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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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Reduce pruning cost to accelerate multimedia kNN search over MBRs based index structures

Reduce pruning cost to accelerate multimedia kNN search over...

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3rd International Conference on Multimedia Information Networking and Security, MINES 2011

作者： Li, Hui Zhang, Xiao Wang, Shan School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

ISBN: (纸本)9780769545592

MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR based index structures. However, with the increase of dimensionality, the computation costs of above two metrics are expensive, and the involved distance computations may become the considerable component. In this paper, to improve the performance of kNN search over multimedia data, we proposed an approach to reduce the computation cost of pruning heuristics by use the cheap upper bound of MINMAXDIST instead of its precise value, and then we derive enhanced cheap heuristics based on this upper bound for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. We conducted an extensive experimental study on a large real-life multimedia dataset called MSRA-MM 2.0, obtained from the commercial Bing Image search engine. The results show that the proposed approach significantly reduced the computation cost and boosted the overall performance of MBR based multimedia kNN search. © 2011 IEEE.

关键词： Cost reduction

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

作者： Liu, Tao Fan, Biao Hu, He Du, Xiao-Yong Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781457703065

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation. © 2011 IEEE.

关键词： Information retrieval

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