Intrusion Detection Method Using Neural Networks Based on the Reduction of Characteristics

The application of techniques based on Artificial Intelligence for intrusion detection systems (IDS), mostly, artificial neural networks (ANN), is becoming a mainstream as well as an extremely effective approach to address some of the current problems in this area. Nevertheless, the selection criteria of the features to be used as inputs for the ANNs remains a problematic issue, which can be put, in a nutshell, as follows: The wider the detection spectrum of selected features is, the lower the performance efficiency of the process becomes and vice versa. This paper proposes sort of a compromise between both ends of the scale: a model based on Principal Component Analysis (PCA) as the chosen algorithm for reducing characteristics in order to maintain the efficiency without hindering the capacity of detection. PCA uses a data model to diminish the size of ANN's input vectors, ensuring a minimum loss of information, and consequently reducing the complexity of the neural classifier as well as maintaining stability in training times. A test scenario for validation purposes was developed, using based-on-ANN IDS. The results obtained based on the tests have demonstrated the validity of the proposal.