  In the last years, Deep Learning technology has been proposed in different
fields, bringing many advances in each of them, but identifying new threats in
these solutions regarding cybersecurity. Those implemented models have brought
several vulnerabilities associated with Deep Learning technology. Moreover,
those allow taking advantage of the implemented model, obtaining private
information, and even modifying the model's decision-making. Therefore,
interest in studying those vulnerabilities/attacks and designing defenses to
avoid or fight them is gaining prominence among researchers. In particular, the
widely known evasion attack is being analyzed by researchers; thus, several
defenses to avoid such a threat can be found in the literature. Since the
presentation of the L-BFG algorithm, this threat concerns the research
community. However, it continues developing new and ingenious countermeasures
since there is no perfect defense for all the known evasion algorithms. In this
work, a novel detector of evasion attacks is developed. It focuses on the
information of the activations of the neurons given by the model when an input
sample is injected. Moreover, it puts attention to the topology of the targeted
deep learning model to analyze the activations according to which neurons are
connecting. This approach has been decided because the literature shows that
the targeted model's topology contains essential information about if the
evasion attack occurs. For this purpose, a huge data preprocessing is required
to introduce all this information in the detector, which uses the Graph
Convolutional Neural Network (GCN) technology. Thus, it understands the
topology of the target model, obtaining promising results and improving the
outcomes presented in the literature related to similar defenses.
