Anomaly Detection Through Quasi-Functional Dependency Analysis
Giulia Bruno, Paolo Garza, Elisa Quintarelli, Rosalba Rossato · PORTO Publications Open Repository TOrino (Politecnico di Torino) · 2007
Anomaly detection problems have been investigated in several research areas such as database, machine learning, knowledge discovery, and logic programming, with the main goal of identifying objects of a given population whose is anomalous with respect to a set of commonly accepted that are part of the knowledge base. In this paper we focus our attention on the analysis of anomaly detection in databases. We propose a method, based on data mining algorithms, which allows one to infer the normal behavior of objects, by extracting frequent rules from a given dataset. These are described in the form of quasi- functional dependencies and mined from the dataset by using association rules. Our approach allows us to consequently analyze anomalies with respect to the previously inferred dependencies: given a quasi-functional dependency, it is possible to discover the related anomalies by querying either the original database or the association previously stored. By further investigating the nature of such anomalies, we can either derive the presence of erroneous data or highlight novel information which represents significant exceptions of frequent rules. Our method is independent of the considered database and directly infers from the data. The applicability of the proposed approach is validated through a set of experiments on XML databases, whose results are here reported.