Training strategy to improve the efficiency of an intelligent detection system

Abstract:

Detection systems of computer accesses are essential for information security. In this article we propose a classification system that combines two intelligent algorithms: Supervised Classification Systems, UCS, and Decision Trees, C4.5. The experiments were carried out using a dataset provided by Amazon, the Kaggle challenge. The system has been trained by dividing the dataset into subgroup. This training strategy has resulted more efficient than if the whole database is used as an only set. Results prove that the use of the proposed detection system provides higher classification accuracy and reduces the percentage of false positives in comparison to other classification techniques.

Año de publicación:

2014

Keywords:

Fuente:

scopus