Adversarial Malware Generation (AMG), the gen- eration of adversarial malware
variants to strengthen Deep Learning (DL)-based malware detectors has emerged
as a crucial tool in the development of proactive cyberdefense. However, the
majority of extant works offer subtle perturbations or additions to executable
files and do not explore full-file obfuscation. In this study, we show that an
open-source encryption tool coupled with a Reinforcement Learning (RL)
framework can successfully obfuscate malware to evade state-of-the-art malware
detection engines and outperform techniques that use advanced modification
methods. Our results show that the proposed method improves the evasion rate
from 27%-49% compared to widely- used state-of-the-art reinforcement
learning-based methods.

通过使用开源加密工具和强化学习框架，成功地对恶意软件进行了混淆，以逃避最先进的恶意软件检测引擎，并且在使用高级修改方法的技术中表现优异，提高了逃避率。

通过混淆机制规避基于深度学习的恶意软件检测器：一种深度强化学习方法

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep  Reinforcement Learning Approach

Malware detection models based on deep learning have been widely used, but
recent research shows that deep learning models are vulnerable to adversarial
attacks. Adversarial attacks are to deceive the deep learning model by
generating adversarial samples. When adversarial attacks are performed on the
malware detection model, the attacker will generate adversarial malware with
the same malicious functions as the malware, and make the detection model
classify it as benign software. Studying adversarial malware generation can
help model designers improve the robustness of malware detection models. At
present, in the work on adversarial malware generation for byte-to-image
malware detection models, there are mainly problems such as large amount of
injection perturbation and low generation efficiency. Therefore, this paper
proposes FGAM (Fast Generate Adversarial Malware), a method for fast generating
adversarial malware, which iterates perturbed bytes according to the gradient
sign to enhance adversarial capability of the perturbed bytes until the
adversarial malware is successfully generated. It is experimentally verified
that the success rate of the adversarial malware deception model generated by
FGAM is increased by about 84\% compared with existing methods.

本文提出了 FGAM 方法，用于快速生成对抗恶意软件，通过迭代扰动字节以提高对抗能力，实验证明该方法相较于现有方法，成功生成的对抗恶意软件欺骗模型的成功率提高了约 84％。