Horacio Daniel Kuna

Outlier detection in audit logs for application systems

Abstract An outlier is defined as an observation that is significantly different from the other data in its set. An auditor will employ many techniques, processes and tools to identify these entries, and data mining is one such medium through which the auditor can analyze information. The enormous amount of information contained within transactional processing systems׳ logs means that auditors must employ automated systems for anomalous data detection. Several data mining algorithms have been tested, especially those that deal specifically with classification and outlier detection. A group of these previously described algorithms was selected for use in designing and developing a process to assist the auditor in anomalous data detection within audit logs. We have been successful in creating and ratifying an outlier detection process that works in the alphanumeric fields of the audit logs from an information system, thus constituting a useful tool for system auditors performing data analysis tasks.

Inliers, outliers and fuzzy prototypes of the user profiles in a scientific metasearcher

Inliers and Outliers are widely studied in the world of databases. In the early 80s, L. A. Zadeh proposed an interesting approach to the concept of ‘prototype’ which contrasts with the classical theories of prototypes from the field of cognitive psychology. This definition has not been used much in fuzzy scientific proposals. Metasearch engines are often designed and used for Information Retrieval tasks because they are cheap and easy to develop, as they get their results from multiple search engines. In this paper, a definition of Inliers and Outliers is presented and related to Zadehs concept of fuzzy prototype: Inliers with the prototype of the borderline elements and Outliers with the prototype of the bad elements of a dataset. Then, this approach is used for dealing with the User Profiles in a real Scientific Metasearcher with the aim of proposing recommendations, sending warnings and in general enriching the management of these User Profiles. An example and experiments proposal in this real system are also presented.