An advanced methodology to analyse data stored on mobile devices

Nowadays computer and mobile devices, such as mobile phones, smartphones, smartwatches, tablets, etc., represent the multimedia diary of each of us. Thanks to technological evolution and the advent of an infinite number of applications, mainly aimed at socialization and entertainment, they have become the containers of an infinite number of personal and professional information. For this reason, optimizing the performance of systems able to detect intrusions (IDS - Intrusion Detection System) is a goal of common interest. This paper presents a methodology to classify hacking attacks taking advantage of the generalization property of neural networks. In particular, in this work we adopt the multilayer perceptron (MLP) model with the back-propagation algorithm and the sigmoidal activation function. We analyse the results obtained using different configurations for the neural network, varying the number of hidden layers and the number of training epochs in order to obtain a low number of false positives. The obtained results will be presented in terms of type of attacks and training epochs and we will show that the best classification is carried out for DOS and Probe attacks.