arXiv subjects

Fabio Di Troia

Publications and source records attributed to Fabio Di Troia.

21 records · Page 2Linked to original sources

arXiv2019

Transfer Learning for Image-Based Malware Classification

In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive image datasets. We carry out various experiments with this technique and compare its performance to that of an extremely simple machine learning technique, namely, k-nearest neighbors (\kNN). For our k-NN experiments, we use features extracted directly from executables, rather than image analysis. While our image-based DL technique performs well in the experiments, surprisingly, it is outperformed by k-NN. We show that DL models are better able to generalize the data, in the sense that they outperform k-NN in simulated zero-day experiments.

cs.LG↗

arXiv2019

A Comparative Analysis of Android Malware

In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary classification of malware versus benign, as well as the multiclass problem, where we classify malware samples into their respective families. Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, AdaBoost, and artificial neural networks. We find that permissions are a strong feature and that by careful feature engineering, we can significantly reduce the number of features needed for highly accurate detection and classification.

cs.CR↗

arXiv2019

Malware Detection Using Dynamic Birthmarks

In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls. We contrast our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in contrasting our two dynamic analysis techniques, we find that using PHMMs consistently outperforms our analysis based on HMMs.

cs.CR↗