  Fully Connected Neural Networks (FCNNs) have been the core of most
state-of-the-art Machine Learning (ML) applications in recent years and also
have been widely used for Intrusion Detection Systems (IDSs). Experimental
results from the last years show that generally deeper neural networks with
more layers perform better than shallow models. Nonetheless, with the growing
number of layers, obtaining fast predictions with less resources has become a
difficult task despite the use of special hardware such as GPUs. We propose a
new architecture to detect network attacks with minimal resources. The
architecture is able to deal with either binary or multiclass classification
problems and trades prediction speed for the accuracy of the network. We
evaluate our proposal with two different network intrusion detection datasets.
Results suggest that it is possible to obtain comparable accuracies to simple
FCNNs without evaluating all layers for the majority of samples, thus obtaining
early predictions and saving energy and computational efforts.
