Banca de DEFESA: ADEILSON ANTONIO DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : ADEILSON ANTONIO DA SILVA
DATE: 22/11/2022
TIME: 14:00
LOCAL: Virtual/Google Meet
TITLE:

On deceiving malware classification with section injection: attack and defense using deep neural networks.

KEY WORDS:

Malware classification; adversarial examples; deep learning; convolutional neural networks.

PAGES: 100
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUMMARY:

We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but also as a defensive method, augmenting the data available for training. It respects the operating system file format to make sure the malware will still execute after our injection and will not change its behavior. We reproduced five state-of-the-art malware classification approaches to evaluate our injection scheme: one based on GIST+KNN, three CNN variations and one Gated CNN. We performed our experiments on a public dataset with 9,339 malware samples from 25 different families. Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification. They show that a automatic malware classification system may not be as trustworthy as initially reported in the literature. We also evaluate using modified malwares alongside the original ones to increase networks robustness against mentioned attacks. Results show that a combination of reordering malware sections and injecting random data can improve overall performance of the classification. Code available at https://github.com/adeilsonsilva/malware-injection.

COMMITTEE MEMBERS:
Externo à Instituição - ANDRÉ BRASIL VIEIRA WYZYKOWSKI
Externo ao Programa - 2263639 - KARL PHILIPS APAZA AGUERO - nullExterno à Instituição - MAURICIO PAMPLONA SEGUNDO