iosr-JCE   Volume-14 ~ Issue-5

Abstract: Many organizations are working hard to secure themselves from the growing threats of message hacking through various trends in cryptography. Yet the headlines are dominated with the latest news of message passing disaster more frequently than any time before. This document intends to review this problem and propose several possible solutions. The cryptographic industry has been responding to these threats with ever-quicker responses to the rapid onslaught of malicious techniques, while corporations establish strict cryptographic techniques.

Index Terms- Security Threats, Cryptosystems, Ciph Downline Load Security ertext,Encryption,Decryption, Interception, Interruption, Fabrication, Authentication, Password Hashing.

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Abstract: Knowledge management is expected to be integral part any software development and services companies. Knowledge has become an important capital for many organizations in the international competition. So knowledge management is gradually becoming the core competence and key of sustainable development for organizations. Many enterprises have already used concepts and methods of knowledge management for operation and achieved remarkable results. Based on the analysis of knowledge management system, a framework model for enterprise knowledge management is presented in this paper. For an enterprise, it is necessary to build this knowledge management system to share knowledge resources, provide scientific supports for decision-making, face fiercely competitive market, and so on.

Keywords: Knowledge Management, Software Enterprise, Knowledge Creation, Generation, Acquisition, Application, Distribution, Identification

[1]. Research on Knowledge Management System in Enterprise - Hua Jiang ; Sch. of Economic & Manage., Hebei Univ. of Eng., Handan, China ; Cuiqing Liu ; Zhenxing Cui
[2]. Tacit knowledge - http://en.wikipedia.org/wiki/Tacit_knowledge
[3]. Explicit knowledge - http://en.wikipedia.org/wiki/Explicit_knowledge
[4]. Ref. Nonaka I., Takeuchi H., The Knowledge Creating Company, (1995), Oxford University Press
[5]. Knowledge in software life cycle - Havlice, Z. ; Dept. of Computer& Inf., Tech. Univ. of Kosice, Kosice ; Kunstar, J. ; Adamuscinova, I. ; Plocica, O.
[6]. Nonaka, I. and Takeuchi, H. 1995. The Knowledge-Creating Company. Oxford University Press.
[7]. Dybå, T., Kitchenham, B. A. and Jørgensen, M. 2005. Evidence-Based Software Engineering for Practitioners. IEEE Software 22(1): 58-65.
[8]. http://www.k-strategian.com/knowledge-based-value-creation/
[9]. http://en.wikipedia.org/wiki/Knowledge_organization_(management)
[10]. http://www.epistemics.co.uk/Notes/40-0-0.htm

Abstract: Historically solving crimes has been the privilege of the criminal justice and law enforcement specialists. With the increasing use of the computerized systems to track crimes, computer data analysts have started helping the law enforcement officers and detectives to speed up the process of solving crimes. According to, solving crimes is a complex task that requires human intelligence and experience. In this research we belief data mining is a technique that can assist law enforcement officers with crime detection problems, so the proposal tries to benefits years of human experience into computer models via data mining. Here we will take an interdisciplinary approach between computer science and criminal justice to develop a proposed data mining model. The proposed model is a three correlated dimensional model; each dimension is a datasets, first one present crime dataset second present criminal dataset and the third present geo-crime dataset. This model apply the Association Rules AR data mining algorithm on each of the three correlated dataset separately then using Genetic Algorithm GA as mixer of the resulted ARs to exploit the relational patterns among crime, criminal and geo-crime to help to detect universal crimes patterns and speed up the process of solving crime with more accurate. This research introduces suggestion to secure the results of the data mining association rules. For privacy preserving secure datasets we aim to hide the general secure and sensitive rules from appearing as a result of applying AR. This could be done by making the confidence of secure rules equal to zero by modifying the supports of critical and sensitive items in these rules. The proposal applied on real crime data from a dependable sheriff's office and validated our results.

Keywords: Crime Analysis, Data Mining, AR, GA, Crimnal.

[1]. Chen H. , Chung W. , Qin Y., Chau l. , Xu J.ennifer, Wang G., Zheng R., Atabakhsh H., "Crime Data Mining: An Overview and Case Studies", AI Lab, University of Arizona, proceedings National Conference on Digital Government Research, 2003, available at: http://ai.bpa.arizona.edu/
[2]. Fayyad U.M. and Uthurusamy R. ," Evolving data mining into solutions for insights". Communications of the ACM, 45(8), 28-31, 2002.
[3]. Chau M., Xu J., and Chen H., "Extracting meaningful entities from police narrative reports". In: Proceedings of the National Conference for Digital Government Research (dg.o 2002), Los Angeles, California, USA.