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[9] T. Huynh, N. Jennings, and N. Shadbolt, An Integrated Trust and Reputation Model for Open Multi-Agent Systems. Autonomous Agents and Multiagent Systems, vol. 13, no. 2, 2006, pp. 119–154. [10]Marios D. Dikaiakos and George Pallis Dimitrios Katsaros Pankaj Mehra Athena Vakali . Cloud Computing :Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing.Serptmeber/October 2009.pp.10-13. [11]WANG Liang, GUO Ya-jun1. Trust Modeling and Evaluation in E-commerce System. Computer Engineering, Vol.35 No.10, May 2009,pp.129-131. [12]S.D. Kamvar, M.T. Schlosser, and H. Garcia-Molina,“The EigenTrust Algorithm for Reputation Management in P2P Networks,” Proc. World Wide Web Conf. (WWW03), ACM Press, 2003, pp. 640–651. [13]B. Yu, M.P. Singh, and K. Sycara, “Developing Trust in Large-Scale Peer-to-Peer Systems,” Proc. IEEE 1 st Symp. Multi-Agent Security and Survivability, IEEE CS Press, 2004, pp. 1–10. [14]Y. Wang and V. Varadharajan, “Role-Based Recommendation and Trust Evaluation,” Proc. IEEE Joint Conf. E-Commerce Technology (CEC 07) and Enterprise Computing, E-Commerce, and E-Services (IEEE 07), IEEE CS Press, 2007, pp. 278–295. [15]F. Martino and A. Spoto, “Social Network Analysis: A Brief Theoretical Review and Further Perspectives in the Study of Information Technology,” PsychNology J.,vol. 4, no. 1, 2006, pp. 53–86. [16]S. Staab et al., “Social Networks Applied,” IEEE Intelligent Systems, vol. 20, no. 1, 2005, pp. 80–93. 274
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 275-281 Database Auditing Design on Historical Data Narongrit Waraporn School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand Email: narongrit@sit.kmutt.ac.th Abstract—Database Auditing is one of the major issues in information security. Lack of data auditing leads the business applications to the lost trail of business processes. To cope with auditing, historical data or temporal database is needed in order to track operations and actors of the operation with the time. Valid and transaction times are two major timestamps in temporal database. In this paper, we demonstrate the techniques to handle database auditing in business transaction by considering operations, valid times, and actors of the operations. These techniques are divided in two sets; using relational databases, and using semistructure data. Index Terms— Database Auditing, Historical Data, Temporal Database, Object-Relational Database, XML I. INTRODUCTION Information System, IS, and Information Retrieval, IR, have been defined and adopted into the practices since the beginning of digital revolution. While information has been used for business processes, information security emerged in term of authentication and authorization. Those concepts can protect information but do not provide help in investigation. Then, the log files were proposed to track the trail of access to databases and systems. However, the main purpose of database log is to recovery the transaction. To be able to investigate the transaction, the database audit was introduced. Many commercial databases suggest database auditing by using the log files. DB2 offers the creation of “audit policy” to apply to certain roles of users. However, they cannot cover every aspect of business processes when considering historical data. We propose techniques of database auditing based on historical data for proprietary systems. In some transactional systems, database design includes auditing data into operational table. However, the auditing data is not used often. We propose the separation between auditing data and business operation data. This paper is organized into three main parts. First, the fundamental and reviews of auditing and historical data are in section II and section III respectively. Second, the design of database auditing is presented in various approaches. Using well-established relational databases to implement is suggested with three approaches; rowbased, column-based and log-table auditing, in section IV. While semi-structured database serves the complex information, we suggest the use of two alternatives; Object-Relational Database and XML in section V. At last, we conclude and discuss other issues of database auditing in section VI. © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 275 II. AUDITING Auditing a change to a management system such as accounting, performance, and software, has been well defined and proctored to improve the quality. One of the major sources of auditing is data where auditor can find the errors and misuses. Lately, data, itself, is audited for its movement to identify malicious behavior, maintain data quality, and improve system performance in [7], and [13]. [7] proposed a framework for auditing the changes to a database system with the retention policies. The declarative rules for expressing retentions, semantics for audit query answers, and performance of audit query under retention restrictions were demonstrated. To detect anomaly behavior, [13] suggested a grid-based clustering method on a transaction in an audit data stream containing a set of user activities at a particular time. Since auditing data is likely to be abundant, searching for an auditing answer requires data mining techniques such as classification and regression. [8] presented an auditing data tool based on data mining algorithms. They evaluated their QUality Information System (QUIS) database in different alternatives, instance based classifiers, naive Bayes classifiers, classification rule inducers, and decision trees. To extend auditing concept into communities, [16] developed a hand-on experience on database security and auditing on relational table containing an audit trail of all operations that change data on the target table. But, the paper did not specify the design of audit trail table while our paper suggests the design alternatives with their pros and cons. III. HISTORICAL DATA One key success of auditing is to be able to track the change on the trail of who made the changed, what operation of the modification, and when it happen. The first two trails can be manipulated in relational model while the last trail requires temporal characteristics to handle. Two different types of time are considered when recording the history of information; valid time and transaction time. [1] suggested that valid and transaction times should guarantee no information loss in any circumstantial transactions. [2] suggested that the transaction time is needed to satisfy the data mining. However, in this paper, we demonstrate only the valid time. Transaction time can be stamped when the “user” column saves the username whom modifies the record. However, temporal databases have not been widely adopted by commercial databases and IT communities
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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support 10 Mb / s. But ENC28J60 onl
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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Eq.4 is NP-hard and can be solved b
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Where: G i is the selection field o
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If there is X j in a generation, wh
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Theorem 2.4([10]). Let L 1 and L 2
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The relation between the lattice im
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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