The dominant approach in contemporary science and industry applications is deep learning. Deep learning consists of many different architectures, including dense, convolutional and recurrent neural networks. In this article we compare deep learning, particularly dense neural networks (DNNs) with the other approach, synergetic models. As a task to compare the mobile application security was chosen. Two datasets were created, sensor values for human beings interacting with smartphones and malicious bot records using emulators. Deep learning and synergetic models were tasked to distinguish them from each other using binary classification. Both models tackled a problem comparatively well, archiving 99% and 85% accuracy respectively. While the deep learning model performed better, the synergetic model excelled in training speed, versatility and results transparency. Pros and cons of both models were addressed in the results section.