Authors:
Saad Al-Ahmadi
1
and
Saud Al-Eyead
2
Affiliations:
1
Computer Science, King Saud University, Riyadh, Saudi Arabia
;
2
Computer Science, Prince Sattam Bin Abdulaziz, Kharj, Saudi Arabia
Keyword(s):
GAN, Machine Learning, Deep Learning, Ensemble Classifier, Adversarial Malware Examples.
Abstract:
The rapid advances in machine learning and deep learning algorithms have led to their adoption to tackle different security problems such as spam, intrusion, and malware detection. Malware is a type of software developed with a malicious intent to damage, exploit, or disable devices, systems, or networks. Malware authors typically operate through black-box sitting when they have a partial knowledge about the targeted detection system. It has been shown that supervised machine learning models are vulnerable to well-crafted adversarial examples. The application domain of malware classification introduces additional constraints in the adversarial sample crafting process compared to the computer vision domain: (1) the input is binary and (2) retaining the visual appearance of the malware application and its intended functionality. In this paper, we have developed a heterogeneous ensemble classifier that combines supervised and unsupervised models to hinder black-box attacks designed by t
wo variants of generative adversarial network (GAN). We experimentally validate its soundness on a corpus of malware and legitimate files.
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