The integration and coupling that result from networking, database systems, and real-time applications are serving to increase concern about security and control in modem computing environments. The establishment and effective monitoring of controls in such settings is essential to guarantee data integrity and to prevent undesired intrusion. It is proposed that the monitoring of system controls is essentially an information-processing activity and that the relational model is a useful formalization of information gathered from central monitoring. Moreover, we posit that the universal data view facilitates auditor analysis without imposing the requirement that he or she have explicit knowledge of the structure of the relational database of monitoring information.
Modern-day auditing represents a decision environment that is both complex and characterized by those elements of imprecise measurability addressed by fuzzy set theory. The use of fuzzy set concepts to address this problem was originally attempted by Cooley and Hicks (1983). They derived a measure of reliability, but did not place the measure in a decision context. This article provides the necessary extension and a computational approach to decision evaluation in this environment.
In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenarios, or signatures. Accordingly, there is a need for more robust and adaptive methods for designing and updating intrusion detection systems. One promising approach is the use of data mining methods for discovering intrusion patterns. Discovered patterns and profiles can be translated into classifiers for detecting deviations from normal usage patterns. Among promising mining methods are association rules, link analysis, and rule-induction algorithms. Our particular contribution is a unique approach to combining association rules with link analysis and a rule-induction algorithm to augment intrusion detection systems.
ABSTRACT Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input‐output behaviors found in classes of decision‐making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative‐response models that can be applied to the same kind of problems addressed by machine‐learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative‐response model and compares its performance to two seminal machine‐learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative‐response model may be a useful alternative for certain problem domains.
Part 1 Databases and their context: database systems and the evolution of database technology bonhomie catering - an introductory database application database systems in the organization. Part 2 Database design: conceptual data modelling the rational data model and relational design. Part 3 Relational database implementation: relational algebra and calculus - foundational languages relational implemetation with SQL relational implementation with query-by-example client/server database systems physical database organization and access. Part 4 Managing the database environment: database administration and control distributed database systems DBMS selection. Part 5 Advanced topics: knowledge-base and object-oriented systems. Part 6 Legacy database systems: the network data model the hierarchical model.
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