Antivirus developers are increasingly embracing machine learning as a key
component of malware defense. While machine learning achieves cutting-edge
outcomes in many fields, it also has weaknesses that are exploited by several
adversarial attack techniques. Many authors have presented both white-box and
black-box generators of adversarial malware examples capable of bypassing
malware detectors with varying success. We propose to combine contemporary
generators in order to increase their potential. Combining different generators
can create more sophisticated adversarial examples that are more likely to
evade anti-malware tools. We demonstrated this technique on five well-known
generators and recorded promising results. The best-performing combination of
AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9%
against top-tier antivirus products. This represents an average improvement of
more than 36% and 627% over using only the AMG-random and MAB-Malware
generators, respectively. The generator that benefited the most from having
another generator follow its procedure was the FGSM injection attack, which
improved the evasion rate on average between 91.97% and 1,304.73%, depending on
the second generator used. These results demonstrate that combining different
generators can significantly improve their effectiveness against leading
antivirus programs.

研究如何通过结合多种生成器来创造更复杂的对抗性恶意软件，以提高其通过抗病毒工具的效果。

将对抗性恶意软件生成器组合以增加逃避率

Combining Generators of Adversarial Malware Examples to Increase Evasion  Rate

Deep learning-based adversarial malware detectors have yielded promising
results in detecting never-before-seen malware executables without relying on
expensive dynamic behavior analysis and sandbox. Despite their abilities, these
detectors have been shown to be vulnerable to adversarial malware variants -
meticulously modified, functionality-preserving versions of original malware
executables generated by machine learning. Due to the nature of these
adversarial modifications, these adversarial methods often use a \textit{single
view} of malware executables (i.e., the binary/hexadecimal view) to generate
adversarial malware variants. This provides an opportunity for the defenders
(i.e., malware detectors) to detect the adversarial variants by utilizing more
than one view of a malware file (e.g., source code view in addition to the
binary view). The rationale behind this idea is that while the adversary
focuses on the binary view, certain characteristics of the malware file in the
source code view remain untouched which leads to the detection of the
adversarial malware variants. To capitalize on this opportunity, we propose
Adversarially Robust Multiview Malware Defense (ARMD), a novel multi-view
learning framework to improve the robustness of DL-based malware detectors
against adversarial variants. Our experiments on three renowned open-source
deep learning-based malware detectors across six common malware categories show
that ARMD is able to improve the adversarial robustness by up to seven times on
these malware detectors.

提出了 Adversarially Robust Multiview Malware Defense (ARMD) 方法，它是一种利用多视角学习框架，可以提高深度学习恶意软件检测器对抗性攻击的能力。在六种常见的恶意软件类别上，ARMD 能够将三个知名的深度学习恶意软件检测器的抗攻击能力提高高达七倍。