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Ingeniare. Revista chilena de ingeniería

On-line version ISSN 0718-3305

Abstract

HERRERA-SEMENETS, Vitali  and  GAGO-ALONSO, Andres. A novel rule generator for intrusion detection based on frequent subgraph mining. Ingeniare. Rev. chil. ing. [online]. 2017, vol.25, n.2, pp.226-234. ISSN 0718-3305.  http://dx.doi.org/10.4067/S0718-33052017000200226.

The current development of technologies has boosted the use of telecommunication services. This fact brings an increase in the volumes of data generated in telecommunication companies. Such data need to be processed in order to detect potential intruders or fraud. The rule evaluation techniques are widely used in these application contexts due to their high effectiveness over known attacks. The incorporation of an automatic rule generator allows it to obtain rules in large volumes of data, for assisting information analysts; thus, the accuracy of intrusion detection is increased. In this paper, an automatic rule generation method is presented, including a strategy based on processing the patterns extracted from a training set and building classification rules. Finally, our proposal is evaluated and compared regarding other classifiers, achieving good results.

Keywords : Automatic rule generation; frequent subgraph mining; intrusion detection.

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