Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos.

Abstract

The mining of association rules of quantitative data has been of great research interest in the area of data mining. With the increasing size of databases, there is a large investment in research in creating algorithms to improve performance related to the amount of rules, its relevance and computational performance. The APRIORI algorithm, traditionally used in the extraction of association rules, was originally created to work with categorical attributes. In order to use continuous attributes, it is necessary to transform the continuous attributes, through discretization, into categorical attributes, where each categorie corresponds to a discrete interval. The more traditional discretization methods produce intervals with sharp boundaries, which may underestimate or overestimate elements near the boundaries of the partitions, therefore inducing an inaccurate semantical representation. One way to address this problem is to create soft partitions with smoothed boundaries. In this work, a fuzzy partition of continuous variables, which is based on fuzzy set theory is used. The algorithms for mining fuzzy association rules (FARM - Fuzzy Association Rule Mining) work with this principle, and, in this work, the FUZZYAPRIORI algorithm is used. In this dissertation, we compare the traditional APRIORI and the FUZZYAPRIORI, through classification results of associative classifiers based on rules extracted by these algorithms. These classifiers were applied to a database of records relating to TCP / IP connections that aims to create an Intrusion Detection System.