Adversarial Training is a proven defense strategy against adversarial
malware. However, generating adversarial malware samples for this type of
training presents a challenge because the resulting adversarial malware needs
to remain evasive and functional. This work proposes an attack framework, EGAN,
to address this limitation. EGAN leverages an Evolution Strategy and Generative
Adversarial Network to select a sequence of attack actions that can mutate a
Ransomware file while preserving its original functionality. We tested this
framework on popular AI-powered commercial antivirus systems listed on
VirusTotal and demonstrated that our framework is capable of bypassing the
majority of these systems. Moreover, we evaluated whether the EGAN attack
framework can evade other commercial non-AI antivirus solutions. Our results
indicate that the adversarial ransomware generated can increase the probability
of evading some of them.

提出了一种名为 EGAN 的攻击框架，该框架利用进化策略和生成对抗网络选择攻击动作序列，以改变勒索软件文件的行为，从而绕过大部分 AI 型商业杀毒系统，并增加了绕过其他商业非 AI 型杀毒解决方案的概率。