Can I read A federated approach to Android malware classification through Perm-Maps on EtoBox?

A federated approach to Android malware classification through Perm-Maps by Gianni D’Angelo; Francesco Palmieri; Antonio Robustelli is a Computer Science article available to read on EtoBox.

What is A federated approach to Android malware classification through Perm-Maps about?

## Abstract In the last decades, mobile-based apps have been increasingly used in several application fields for many purposes involving a high number of human activities. Unfortunately, in addition to this, the number of cyber-attacks related to mobile platforms is increasing day-by-day. However, although advances in Artificial Intelligence science have allowed addressing many aspects of the problem, malware classification tasks are still challenging. For this reason, the following paper aims to propose new special features, called permission maps (Perm-Maps), which combine information related to the Android permissions and their corresponding severity levels. Such features have proven to be very effective in classifying different malware families through the usage of a convolutional neural network. Also, the advantages introduced by the Perm-Maps have been enhanced by a training process based on a federated logic. Experimental results show that the proposed approach achieves up to a 3% improvement in average accuracy with respect to J48 trees and Naive Bayes classifier, and up to 16% compared to multi-layer perceptron classifier. Furthermore, the combined use of Perm-Maps and fed

Who reads A federated approach to Android malware classification through Perm-Maps?

Author

Gianni D’Angelo; Francesco Palmieri; Antonio Robustelli

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)